Data Engineering Podcast

Tobias Macey

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here. read less
TechnologyTechnology

Episodes

Find Out About The Technology Behind The Latest PFAD In Analytical Database Development
4d ago
Find Out About The Technology Behind The Latest PFAD In Analytical Database Development
Summary Building a database engine requires a substantial amount of engineering effort and time investment. Over the decades of research and development into building these software systems there are a number of common components that are shared across implementations. When Paul Dix decided to re-write the InfluxDB engine he found the Apache Arrow ecosystem ready and waiting with useful building blocks to accelerate the process. In this episode he explains how he used the combination of Apache Arrow, Flight, Datafusion, and Parquet to lay the foundation of the newest version of his time-series database. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council (https://www.dataengineeringpodcast.com/data-council) and use code dataengpod20 to register today! Your host is Tobias Macey and today I'm interviewing Paul Dix about his investment in the Apache Arrow ecosystem and how it led him to create the latest PFAD in database design Interview Introduction How did you get involved in the area of data management? Can you start by describing the FDAP stack and how the components combine to provide a foundational architecture for database engines? This was the core of your recent re-write of the InfluxDB engine. What were the design goals and constraints that led you to this architecture? Each of the architectural components are well engineered for their particular scope. What is the engineering work that is involved in building a cohesive platform from those components? One of the major benefits of using open source components is the network effect of ecosystem integrations. That can also be a risk when the community vision for the project doesn't align with your own goals. How have you worked to mitigate that risk in your specific platform? Can you describe the operational/architectural aspects of building a full data engine on top of the FDAP stack? What are the elements of the overall product/user experience that you had to build to create a cohesive platform? What are some of the other tools/technologies that can benefit from some or all of the pieces of the FDAP stack? What are the pieces of the Arrow ecosystem that are still immature or need further investment from the community? What are the most interesting, innovative, or unexpected ways that you have seen parts or all of the FDAP stack used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on/with the FDAP stack? When is the FDAP stack the wrong choice? What do you have planned for the future of the InfluxDB IOx engine and the FDAP stack? Contact Info LinkedIn (https://www.linkedin.com/in/pauldix/) pauldix (https://github.com/pauldix) on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links FDAP Stack Blog Post (https://www.influxdata.com/blog/flight-datafusion-arrow-parquet-fdap-architecture-influxdb/) Apache Arrow (https://arrow.apache.org/) DataFusion (https://arrow.apache.org/datafusion/) Arrow Flight (https://arrow.apache.org/docs/format/Flight.html) Apache Parquet (https://parquet.apache.org/) InfluxDB (https://www.influxdata.com/products/influxdb/) Influx Data (https://www.influxdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/influxdb-timeseries-data-platform-episode-199) Rust Language (https://www.rust-lang.org/) DuckDB (https://duckdb.org/) ClickHouse (https://clickhouse.com/) Voltron Data (https://voltrondata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/voltron-data-apache-arrow-episode-346/) Velox (https://github.com/facebookincubator/velox) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Trino (https://trino.io/) ODBC == Open DataBase Connectivity (https://en.wikipedia.org/wiki/Open_Database_Connectivity) GeoParquet (https://github.com/opengeospatial/geoparquet) ORC == Optimized Row Columnar (https://orc.apache.org/) Avro (https://avro.apache.org/) Protocol Buffers (https://protobuf.dev/) gRPC (https://grpc.io/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Using Trino And Iceberg As The Foundation Of Your Data Lakehouse
Feb 18 2024
Using Trino And Iceberg As The Foundation Of Your Data Lakehouse
Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality. In this episode Dain Sundstrom, CTO of Starburst, explains how the combination of the Trino query engine and the Iceberg table format offer the ease of use and execution speed of data warehouses with the infinite storage and scalability of data lakes. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join in with the event for the global data community, Data Council Austin. From March 26th-28th 2024, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working togethr to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council (https://www.dataengineeringpodcast.com/data-council) today. Your host is Tobias Macey and today I'm interviewing Dain Sundstrom about building a data lakehouse with Trino and Iceberg Interview Introduction How did you get involved in the area of data management? To start, can you share your definition of what constitutes a "Data Lakehouse"? What are the technical/architectural/UX challenges that have hindered the progression of lakehouses? What are the notable advancements in recent months/years that make them a more viable platform choice? There are multiple tools and vendors that have adopted the "data lakehouse" terminology. What are the benefits offered by the combination of Trino and Iceberg? What are the key points of comparison for that combination in relation to other possible selections? What are the pain points that are still prevalent in lakehouse architectures as compared to warehouse or vertically integrated systems? What progress is being made (within or across the ecosystem) to address those sharp edges? For someone who is interested in building a data lakehouse with Trino and Iceberg, how does that influence their selection of other platform elements? What are the differences in terms of pipeline design/access and usage patterns when using a Trino/Iceberg lakehouse as compared to other popular warehouse/lakehouse structures? What are the most interesting, innovative, or unexpected ways that you have seen Trino lakehouses used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data lakehouse ecosystem? When is a lakehouse the wrong choice? What do you have planned for the future of Trino/Starburst? Contact Info LinkedIn (https://www.linkedin.com/in/dainsundstrom/) dain (https://github.com/dain) on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Trino (https://trino.io/) Starburst (https://www.starburst.io/) Presto (https://prestodb.io/) JBoss (https://en.wikipedia.org/wiki/JBoss_Enterprise_Application_Platform) Java EE (https://www.oracle.com/java/technologies/java-ee-glance.html) HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html) S3 (https://aws.amazon.com/s3/) GCS == Google Cloud Storage (https://cloud.google.com/storage?hl=en) Hive (https://hive.apache.org/) Hive ACID (https://cwiki.apache.org/confluence/display/hive/hive+transactions) Apache Ranger (https://ranger.apache.org/) OPA == Open Policy Agent (https://www.openpolicyagent.org/) Oso (https://www.osohq.com/) AWS Lakeformation (https://aws.amazon.com/lake-formation/) Tabular (https://tabular.io/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Delta Lake (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) Debezium (https://debezium.io/) Podcast Episode (https://www.dataengineeringpodcast.com/debezium-change-data-capture-episode-114) Materialized View (https://en.wikipedia.org/wiki/Materialized_view) Clickhouse (https://clickhouse.com/) Druid (https://druid.apache.org/) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Data Sharing Across Business And Platform Boundaries
Feb 11 2024
Data Sharing Across Business And Platform Boundaries
Summary Sharing data is a simple concept, but complicated to implement well. There are numerous business rules and regulatory concerns that need to be applied. There are also numerous technical considerations to be made, particularly if the producer and consumer of the data aren't using the same platforms. In this episode Andrew Jefferson explains the complexities of building a robust system for data sharing, the techno-social considerations, and how the Bobsled platform that he is building aims to simplify the process. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Your host is Tobias Macey and today I'm interviewing Andy Jefferson about how to solve the problem of data sharing Interview Introduction How did you get involved in the area of data management? Can you start by giving some context and scope of what we mean by "data sharing" for the purposes of this conversation? What is the current state of the ecosystem for data sharing protocols/practices/platforms? What are some of the main challenges/shortcomings that teams/organizations experience with these options? What are the technical capabilities that need to be present for an effective data sharing solution? How does that change as a function of the type of data? (e.g. tabular, image, etc.) What are the requirements around governance and auditability of data access that need to be addressed when sharing data? What are the typical boundaries along which data access requires special consideration for how the sharing is managed? Many data platform vendors have their own interfaces for data sharing. What are the shortcomings of those options, and what are the opportunities for abstracting the sharing capability from the underlying platform? What are the most interesting, innovative, or unexpected ways that you have seen data sharing/Bobsled used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data sharing? When is Bobsled the wrong choice? What do you have planned for the future of data sharing? Contact Info LinkedIn (https://www.linkedin.com/in/andyjefferson/?originalSubdomain=de) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Bobsled (https://www.bobsled.co/) OLAP == OnLine Analytical Processing (https://en.wikipedia.org/wiki/Online_analytical_processing) Cassandra (https://cassandra.apache.org/_/index.html) Podcast Episode (https://www.dataengineeringpodcast.com/cassandra-global-scale-database-episode-220) Neo4J (https://neo4j.com/) FTP == File Transfer Protocol (https://en.wikipedia.org/wiki/File_Transfer_Protocol) S3 Access Points (https://aws.amazon.com/s3/features/access-points/) Snowflake Sharing (https://docs.snowflake.com/en/guides-overview-sharing) BigQuery Sharing (https://cloud.google.com/bigquery/docs/authorized-datasets) Databricks Delta Sharing (https://www.databricks.com/product/delta-sharing) DuckDB (https://duckdb.org/) Podcast Episode (https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Tackling Real Time Streaming Data With SQL Using RisingWave
Feb 4 2024
Tackling Real Time Streaming Data With SQL Using RisingWave
Summary Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. RisingWave is a database engine that was created specifically for stream processing, with S3 as the storage layer. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Your host is Tobias Macey and today I'm interviewing Yingjun Wu about the RisingWave database and the intricacies of building a stream processing engine on S3 Interview Introduction How did you get involved in the area of data management? Can you describe what RisingWave is and the story behind it? There are numerous stream processing engines, near-real-time database engines, streaming SQL systems, etc. What is the specific niche that RisingWave addresses? What are some of the platforms/architectures that teams are replacing with RisingWave? What are some of the unique capabilities/use cases that RisingWave provides over other offerings in the current ecosystem? Can you describe how RisingWave is architected and implemented? How have the design and goals/scope changed since you first started working on it? What are the core design philosophies that you rely on to prioritize the ongoing development of the project? What are the most complex engineering challenges that you have had to address in the creation of RisingWave? Can you describe a typical workflow for teams that are building on top of RisingWave? What are the user/developer experience elements that you have prioritized most highly? What are the situations where RisingWave can/should be a system of record vs. a point-in-time view of data in transit, with a data warehouse/lakehouse as the longitudinal storage and query engine? What are the most interesting, innovative, or unexpected ways that you have seen RisingWave used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on RisingWave? When is RisingWave the wrong choice? What do you have planned for the future of RisingWave? Contact Info yingjunwu (https://github.com/yingjunwu) on GitHub Personal Website (https://yingjunwu.github.io/) LinkedIn (https://www.linkedin.com/in/yingjun-wu-4b584536/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links RisingWave (https://risingwave.com/) AWS Redshift (https://aws.amazon.com/redshift/) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Druid (https://druid.apache.org/) Materialize (https://materialize.com/) Spark (https://spark.apache.org/) Trino (https://trino.io/) Snowflake (https://www.snowflake.com/en/) Kafka (https://kafka.apache.org/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209) Postgres (https://www.postgresql.org/) Debezium (https://debezium.io/) Podcast Episode (https://www.dataengineeringpodcast.com/debezium-change-data-capture-episode-114) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Build A Data Lake For Your Security Logs With Scanner
Jan 29 2024
Build A Data Lake For Your Security Logs With Scanner
Summary Monitoring and auditing IT systems for security events requires the ability to quickly analyze massive volumes of unstructured log data. The majority of products that are available either require too much effort to structure the logs, or aren't fast enough for interactive use cases. Cliff Crosland co-founded Scanner to provide fast querying of high scale log data for security auditing. In this episode he shares the story of how it got started, how it works, and how you can get started with it. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Cliff Crosland about Scanner, a security data lake platform for analyzing security logs and identifying issues quickly and cost-effectively Interview Introduction How did you get involved in the area of data management? Can you describe what Scanner is and the story behind it? What were the shortcomings of other tools that are available in the ecosystem? What is Scanner explicitly not trying to solve for in the security space? (e.g. SIEM) A query engine is useless without data to analyze. What are the data acquisition paths/sources that you are designed to work with?- e.g. cloudtrail logs, app logs, etc. What are some of the other sources of signal for security monitoring that would be valuable to incorporate or integrate with through Scanner? Log data is notoriously messy, with no strictly defined format. How do you handle introspection and querying across loosely structured records that might span multiple sources and inconsistent labelling strategies? Can you describe the architecture of the Scanner platform? What were the motivating constraints that led you to your current implementation? How have the design and goals of the product changed since you first started working on it? Given the security oriented customer base that you are targeting, how do you address trust/network boundaries for compliance with regulatory/organizational policies? What are the personas of the end-users for Scanner? How has that influenced the way that you think about the query formats, APIs, user experience etc. for the prroduct? For teams who are working with Scanner can you describe how it fits into their workflow? What are the most interesting, innovative, or unexpected ways that you have seen Scanner used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Scanner? When is Scanner the wrong choice? What do you have planned for the future of Scanner? Contact Info LinkedIn (https://www.linkedin.com/in/cliftoncrosland/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Scanner (https://scanner.dev/) cURL (https://curl.se/) Rust (https://www.rust-lang.org/) Splunk (https://www.splunk.com/) S3 (https://aws.amazon.com/s3/) AWS Athena (https://aws.amazon.com/athena/) Loki (https://grafana.com/oss/loki/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Presto (https://prestodb.io/) Trino (thttps://trino.io/) AWS CloudTrail (https://aws.amazon.com/cloudtrail/) GitHub Audit Logs (https://docs.github.com/en/organizations/keeping-your-organization-secure/managing-security-settings-for-your-organization/reviewing-the-audit-log-for-your-organization) Okta (https://www.okta.com/) Cribl (https://cribl.io/) Vector.dev (https://vector.dev/) Tines (https://www.tines.com/) Torq (https://torq.io/) Jira (https://www.atlassian.com/software/jira) Linear (https://linear.app/) ECS Fargate (https://aws.amazon.com/fargate/) SQS (https://aws.amazon.com/sqs/) Monoid (https://en.wikipedia.org/wiki/Monoid) Group Theory (https://en.wikipedia.org/wiki/Group_theory) Avro (https://avro.apache.org/) Parquet (https://parquet.apache.org/) OCSF (https://github.com/ocsf/) VPC Flow Logs (https://docs.aws.amazon.com/vpc/latest/userguide/flow-logs.html) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Modern Customer Data Platform Principles
Jan 22 2024
Modern Customer Data Platform Principles
Summary Databases and analytics architectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern customer data platform (CDP). Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). That’s three free boards at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). Your host is Tobias Macey and today I'm interviewing Tasso Argyros about the role of a customer data platform in the context of the modern data stack Interview Introduction How did you get involved in the area of data management? Can you describe what the role of the CDP is in the context of a businesses data ecosystem? What are the core technical challenges associated with building and maintaining a CDP? What are the organizational/business factors that contribute to the complexity of these systems? The early days of CDPs came with the promise of "Customer 360". Can you unpack that concept and how it has changed over the past ~5 years? Recent years have seen the adoption of reverse ETL, cloud data warehouses, and sophisticated product analytics suites. How has that changed the architectural approach to CDPs? How have the architectural shifts changed the ways that organizations interact with their customer data? How have the responsibilities shifted across different roles? What are the governance policy and enforcement challenges that are added with the expansion of access and responsibility? What are the most interesting, innovative, or unexpected ways that you have seen CDPs built/used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDPs? When is a CDP the wrong choice? What do you have planned for the future of ActionIQ? Contact Info LinkedIn (https://www.linkedin.com/in/tasso/) @Tasso (https://twitter.com/tasso) on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Action IQ (https://www.actioniq.com) Aster Data (https://en.wikipedia.org/wiki/Aster_Data_Systems) Teradata (https://www.teradata.com/) Filemaker (https://en.wikipedia.org/wiki/FileMaker) Hadoop (https://hadoop.apache.org/) NoSQL (https://en.wikipedia.org/wiki/NoSQL) Hive (https://hive.apache.org/) Informix (https://en.wikipedia.org/wiki/Informix) Parquet (https://parquet.apache.org/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Spark (https://spark.apache.org/) Redshift (https://aws.amazon.com/redshift/) Unity Catalog (https://www.databricks.com/product/unity-catalog) Customer Data Platform (https://en.wikipedia.org/wiki/Customer_data_platform) CDP Market Guide (https://info.actioniq.com/hubfs/CDP%20Market%20Guide/CDP_Market_Guide_2024.pdf?utm_campaign=FY24Q4_2024%20CDP%20Market%20Guide&utm_source=AIQ&utm_medium=podcast) Kaizen (https://en.wikipedia.org/wiki/Kaizen) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Pushing The Limits Of Scalability And User Experience For Data Processing WIth Jignesh Patel
Jan 7 2024
Pushing The Limits Of Scalability And User Experience For Data Processing WIth Jignesh Patel
Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. As the sophistication increases, so does the complexity, leading to challenges for user experience. Jignesh Patel has been researching these areas for several years in his work as a professor at Carnegie Mellon University. In this episode he illuminates the landscape of problems that we are faced with and how his research is aimed at helping to solve these problems. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Jignesh Patel about the research that he is conducting on technical scalability and user experience improvements around data management Interview Introduction How did you get involved in the area of data management? Can you start by summarizing your current areas of research and the motivations behind them? What are the open questions today in technical scalability of data engines? What are the experimental methods that you are using to gain understanding in the opportunities and practical limits of those systems? As you strive to push the limits of technical capacity in data systems, how does that impact the usability of the resulting systems? When performing research and building prototypes of the projects, what is your process for incorporating user experience into the implementation of the product? What are the main sources of tension between technical scalability and user experience/ease of comprehension? What are some of the positive synergies that you have been able to realize between your teaching, research, and corporate activities? In what ways do they produce conflict, whether personally or technically? What are the most interesting, innovative, or unexpected ways that you have seen your research used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on research of the scalability limits of data systems? What is your heuristic for when a given research project needs to be terminated or productionized? What do you have planned for the future of your academic research? Contact Info Website (https://jigneshpatel.org/) LinkedIn (https://www.linkedin.com/in/jigneshmpatel/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Carnegie Mellon Universe (https://www.cmu.edu/) Parallel Databases (https://en.wikipedia.org/wiki/Parallel_database) Genomics (https://en.wikipedia.org/wiki/Genomics) Proteomics (https://en.wikipedia.org/wiki/Proteomics) Moore's Law (https://en.wikipedia.org/wiki/Moore%27s_law) Dennard Scaling (https://en.wikipedia.org/wiki/Dennard_scaling) Generative AI (https://en.wikipedia.org/wiki/Generative_artificial_intelligence) Quantum Computing (https://en.wikipedia.org/wiki/Quantum_computing) Voltron Data (https://voltrondata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/voltron-data-apache-arrow-episode-346/) Von Neumann Architecture (https://en.wikipedia.org/wiki/Von_Neumann_architecture) Two's Complement (https://en.wikipedia.org/wiki/Two%27s_complement) Ottertune (https://ottertune.com/) Podcast Episode (https://www.dataengineeringpodcast.com/ottertune-database-performance-optimization-episode-197/) dbt (https://www.getdbt.com/) Informatica (https://www.informatica.com/) Mozart Data (https://mozartdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/mozart-data-modern-data-stack-episode-242/) DataChat (https://datachat.ai/) Von Neumann Bottleneck (https://www.techopedia.com/definition/14630/von-neumann-bottleneck) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Designing Data Platforms For Fintech Companies
Jan 1 2024
Designing Data Platforms For Fintech Companies
Summary Working with financial data requires a high degree of rigor due to the numerous regulations and the risks involved in security breaches. In this episode Andrey Korchack, CTO of fintech startup Monite, discusses the complexities of designing and implementing a data platform in that sector. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Andrey Korchak about how to manage data in a fintech environment Interview Introduction How did you get involved in the area of data management? Can you start by summarizing the data challenges that are particular to the fintech ecosystem? What are the primary sources and types of data that fintech organizations are working with? What are the business-level capabilities that are dependent on this data? How do the regulatory and business requirements influence the technology landscape in fintech organizations? What does a typical build vs. buy decision process look like? Fraud prediction in e.g. banks is one of the most well-established applications of machine learning in industry. What are some of the other ways that ML plays a part in fintech? How does that influence the architectural design/capabilities for data platforms in those organizations? Data governance is a notoriously challenging problem. What are some of the strategies that fintech companies are able to apply to this problem given their regulatory burdens? What are the most interesting, innovative, or unexpected approaches to data management that you have seen in the fintech sector? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data in fintech? What do you have planned for the future of your data capabilities at Monite? Contact Info LinkedIn (https://www.linkedin.com/in/a-korchak/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Monite (https://monite.com/) ISO 270001 (https://www.iso.org/standard/27001) Tesseract (https://github.com/tesseract-ocr/tesseract) GitOps (https://about.gitlab.com/topics/gitops/) SWIFT Protocol (https://en.wikipedia.org/wiki/SWIFT) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Troubleshooting Kafka In Production
Dec 24 2023
Troubleshooting Kafka In Production
Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Elad Eldor about operating Kafka in production and how to keep your clusters stable and performant Interview Introduction How did you get involved in the area of data management? Can you describe your experiences with Kafka? What are the operational challenges that you have had to overcome while working with Kafka? What motivated to write a book about how to manage Kafka in production? There are many options now for persistent data queues. What are the factors to consider when determining whether Kafka is the right choice? In the case where Kafka is the appropriate tool, there are many ways to run it now. What are the considerations that teams need to work through when determining whether/where/how to operate a cluster? When provisioning a Kafka cluster, what are the requirements that need to be considered when determining the sizing? What are the axes along which size/scale need to be determined? The core promise of Kafka is that it is a durable store for continuous data. What are the mechanisms that are available for preventing data loss? Under what circumstances can data be lost? What are the different failure conditions that cluster operators need to be aware of? What are the monitoring strategies that are most helpful for identifying (proactively or reactively) those errors? In the event of these different cluster errors, what are the strategies for mitigating and recovering from those failures? When a cluster's usage expands beyond the original designed capacity, what are the options/procedures for expanding that capacity? When a cluster is underutilized, how can it be scaled down to reduce cost? What are the most interesting, innovative, or unexpected ways that you have seen Kafka used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with Kafka? When is Kafka the wrong choice? What are the changes that you would like to see in Kafka to make it easier to operate? Contact Info LinkedIn (https://www.linkedin.com/in/elad-eldor/?originalSubdomain=il) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Kafka: Troubleshooting in Production (https://amzn.to/3NFzPgL) book (affiliate link) IronSource (https://www.is.com/) Druid (https://druid.apache.org/) Trino (https://trino.io/) Kafka (https://kafka.apache.org/) Spark (https://spark.apache.org/) SRE == Site Reliability Engineer (https://en.wikipedia.org/wiki/Site_reliability_engineering) Presto (https://prestodb.io/) System Performance (https://amzn.to/3tkQAag) by Brendan Gregg (affiliate link) HortonWorks (https://en.wikipedia.org/wiki/Hortonworks) RAID == Redundant Array of Inexpensive Disks (https://en.wikipedia.org/wiki/RAID) JBOD == Just a Bunch Of Disks (https://en.wikipedia.org/wiki/Non-RAID_drive_architectures#JBOD) AWS MSK (https://aws.amazon.com/msk/) Confluent (https://www.confluent.io/) Aiven (https://aiven.io/) JStat (https://docs.oracle.com/javase/8/docs/technotes/tools/windows/jstat.html) Kafka Tiered Storage (https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage) Brendan Gregg iostat utilization explanation (https://www.brendangregg.com/blog/2021-05-09/poor-disk-performance.html) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Adding An Easy Mode For The Modern Data Stack With 5X
Dec 18 2023
Adding An Easy Mode For The Modern Data Stack With 5X
Summary The "modern data stack" promised a scalable, composable data platform that gave everyone the flexibility to use the best tools for every job. The reality was that it left data teams in the position of spending all of their engineering effort on integrating systems that weren't designed with compatible user experiences. The team at 5X understand the pain involved and the barriers to productivity and set out to solve it by pre-integrating the best tools from each layer of the stack. In this episode founder Tarush Aggarwal explains how the realities of the modern data stack are impacting data teams and the work that they are doing to accelerate time to value. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm welcoming back Tarush Aggarwal to talk about what he and his team at 5x data are building to improve the user experience of the modern data stack. Interview Introduction How did you get involved in the area of data management? Can you describe what 5x is and the story behind it? We last spoke in March of 2022. What are the notable changes in the 5x business and product? What are the notable shifts in the data ecosystem that have influenced your adoption and product direction? What trends are you most focused on tracking as you plan the continued evolution of your offerings? What are the points of friction that teams run into when trying to build their data platform? Can you describe design of the system that you have built? What are the strategies that you rely on to support adaptability and speed of onboarding for new integrations? What are some of the types of edge cases that you have to deal with while integrating and operating the platform implementations that you design for your customers? What is your process for selection of vendors to support? How would you characterize your relationships with the vendors that you rely on? For customers who have pre-existing investment in a portion of the data stack, what is your process for engaging with them to understand how best to support their goals? What are the most interesting, innovative, or unexpected ways that you have seen 5XData used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on 5XData? When is 5X the wrong choice? What do you have planned for the future of 5X? Contact Info LinkedIn (https://www.linkedin.com/in/tarushaggarwal/) @tarush (https://twitter.com/tarush) on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links 5X (https://5x.co) Informatica (https://www.informatica.com/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Looker (https://cloud.google.com/looker/) Podcast Episode (https://www.dataengineeringpodcast.com/looker-with-daniel-mintz-episode-55/) DuckDB (https://duckdb.org/) Podcast Episode (https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/) Redshift (https://aws.amazon.com/redshift/) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) Rudderstack (https://www.rudderstack.com/) Podcast Episode (https://www.dataengineeringpodcast.com/rudderstack-open-source-customer-data-platform-episode-263/) Peak.ai (https://peak.ai/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Run Your Own Anomaly Detection For Your Critical Business Metrics With Anomstack
Dec 11 2023
Run Your Own Anomaly Detection For Your Critical Business Metrics With Anomstack
Summary If your business metrics looked weird tomorrow, would you know about it first? Anomaly detection is focused on identifying those outliers for you, so that you are the first to know when a business critical dashboard isn't right. Unfortunately, it can often be complex or expensive to incorporate anomaly detection into your data platform. Andrew Maguire got tired of solving that problem for each of the different roles he has ended up in, so he created the open source Anomstack project. In this episode he shares what it is, how it works, and how you can start using it today to get notified when the critical metrics in your business aren't quite right. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). That’s three free boards at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrew Maguire about his work on the Anomstack project and how you can use it to run your own anomaly detection for your metrics Interview Introduction How did you get involved in the area of data management? Can you describe what Anomstack is and the story behind it? What are your goals for this project? What other tools/products might teams be evaluating while they consider Anomstack? In the context of Anomstack, what constitutes a "metric"? What are some examples of useful metrics that a data team might want to monitor? You put in a lot of work to make Anomstack as easy as possible to get started with. How did this focus on ease of adoption influence the way that you approached the overall design of the project? What are the core capabilities and constraints that you selected to provide the focus and architecture of the project? Can you describe how Anomstack is implemented? How have the design and goals of the project changed since you first started working on it? What are the steps to getting Anomstack running and integrated as part of the operational fabric of a data platform? What are the sharp edges that are still present in the system? What are the interfaces that are available for teams to customize or enhance the capabilities of Anomstack? What are the most interesting, innovative, or unexpected ways that you have seen Anomstack used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anomstack? When is Anomstack the wrong choice? What do you have planned for the future of Anomstack? Contact Info LinkedIn (https://www.linkedin.com/in/andrewm4894/) Twitter (https://twitter.com/@andrewm4894) GitHub (http://github.com/andrewm4894) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Anomstack Github repo (http://github.com/andrewm4894/anomstack) Airflow Anomaly Detection Provider Github repo (https://github.com/andrewm4894/airflow-provider-anomaly-detection) Netdata (https://www.netdata.cloud/) Metric Tree (https://www.datacouncil.ai/talks/designing-and-building-metric-trees) Semantic Layer (https://en.wikipedia.org/wiki/Semantic_layer) Prometheus (https://prometheus.io/) Anodot (https://www.anodot.com/) Chaos Genius (https://www.chaosgenius.io/) Metaplane (https://www.metaplane.dev/) Anomalo (https://www.anomalo.com/) PyOD (https://pyod.readthedocs.io/) Airflow (https://airflow.apache.org/) DuckDB (https://duckdb.org/) Anomstack Gallery (https://github.com/andrewm4894/anomstack/tree/main/gallery) Dagster (https://dagster.io/) InfluxDB (https://www.influxdata.com/) TimeGPT (https://docs.nixtla.io/docs/timegpt_quickstart) Prophet (https://facebook.github.io/prophet/) GreyKite (https://linkedin.github.io/greykite/) OpenLineage (https://openlineage.io/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Designing Data Transfer Systems That Scale
Dec 4 2023
Designing Data Transfer Systems That Scale
Summary The first step of data pipelines is to move the data to a place where you can process and prepare it for its eventual purpose. Data transfer systems are a critical component of data enablement, and building them to support large volumes of information is a complex endeavor. Andrei Tserakhau has dedicated his careeer to this problem, and in this episode he shares the lessons that he has learned and the work he is doing on his most recent data transfer system at DoubleCloud. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues for every part of your data workflow, from migration to deployment. Datafold has recently launched a 3-in-1 product experience to support accelerated data migrations. With Datafold, you can seamlessly plan, translate, and validate data across systems, massively accelerating your migration project. Datafold leverages cross-database diffing to compare tables across environments in seconds, column-level lineage for smarter migration planning, and a SQL translator to make moving your SQL scripts easier. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) today! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrei Tserakhau about operationalizing high bandwidth and low-latency change-data capture Interview Introduction How did you get involved in the area of data management? Your most recent project involves operationalizing a generalized data transfer service. What was the original problem that you were trying to solve? What were the shortcomings of other options in the ecosystem that led you to building a new system? What was the design of your initial solution to the problem? What are the sharp edges that you had to deal with to operate and use that initial implementation? What were the limitations of the system as you started to scale it? Can you describe the current architecture of your data transfer platform? What are the capabilities and constraints that you are optimizing for? As you move beyond the initial use case that started you down this path, what are the complexities involved in generalizing to add new functionality or integrate with additional platforms? What are the most interesting, innovative, or unexpected ways that you have seen your data transfer service used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data transfer system? When is DoubleCloud Data Transfer the wrong choice? What do you have planned for the future of DoubleCloud Data Transfer? Contact Info LinkedIn (https://www.linkedin.com/in/andrei-tserakhau/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links DoubleCloud (https://double.cloud/) Kafka (https://kafka.apache.org/) MapReduce (https://en.wikipedia.org/wiki/MapReduce) Change Data Capture (https://en.wikipedia.org/wiki/Change_data_capture) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/iceberg-with-ryan-blue-episode-52/) Delta Lake (https://delta.io/) Podcast Episode (https://www.dataengineeringpodcast.com/delta-lake-data-lake-episode-85/) dbt (https://www.getdbt.com/) OpenMetadata (https://open-metadata.org/) Podcast Episode (https://www.dataengineeringpodcast.com/openmetadata-universal-metadata-layer-episode-237/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/) Speaker - Andrei Tserakhau, DoubleCloud Tech Lead. He has over 10 years of IT engineering experience and for the last 4 years was working on distributed systems with a focus on data delivery systems.
Addressing The Challenges Of Component Integration In Data Platform Architectures
Nov 27 2023
Addressing The Challenges Of Component Integration In Data Platform Architectures
Summary Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. One of the core complexities that needs to be addressed is the fractal set of integrations that need to be managed across the individual components. In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Developing event-driven pipelines is going to be a lot easier - Meet Functions! Memphis functions enable developers and data engineers to build an organizational toolbox of functions to process, transform, and enrich ingested events “on the fly” in a serverless manner using AWS Lambda syntax, without boilerplate, orchestration, error handling, and infrastructure in almost any language, including Go, Python, JS, .NET, Java, SQL, and more. Go to dataengineeringpodcast.com/memphis (https://www.dataengineeringpodcast.com/memphis) today to get started! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'll be sharing an update on my own journey of building a data platform, with a particular focus on the challenges of tool integration and maintaining a single source of truth Interview Introduction How did you get involved in the area of data management? data sharing weight of history existing integrations with dbt switching cost for e.g. SQLMesh de facto standard of Airflow Single source of truth permissions management across application layers Database engine Storage layer in a lakehouse Presentation/access layer (BI) Data flows dbt -> table level lineage orchestration engine -> pipeline flows task based vs. asset based Metadata platform as the logical place for horizontal view Contact Info LinkedIn (https://linkedin.com/in/tmacey) Website (https://www.dataengineeringpodcast.com) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Monologue Episode On Data Platform Design (https://www.dataengineeringpodcast.com/data-platform-design-episode-268) Monologue Episode On Leaky Abstractions (https://www.dataengineeringpodcast.com/abstractions-and-technical-debt-episode-374) Airbyte (https://airbyte.com/) Podcast Episode (https://www.dataengineeringpodcast.com/airbyte-open-source-data-integration-episode-173/) Trino (https://trino.io/) Dagster (https://dagster.io/) dbt (https://www.getdbt.com/) Snowflake (https://www.snowflake.com/en/) BigQuery (https://cloud.google.com/bigquery) OpenMetadata (https://open-metadata.org/) OpenLineage (https://openlineage.io/) Data Platform Shadow IT Episode (https://www.dataengineeringpodcast.com/shadow-it-data-analytics-episode-121) Preset (https://preset.io/) LightDash (https://www.lightdash.com/) Podcast Episode (https://www.dataengineeringpodcast.com/lightdash-exploratory-business-intelligence-episode-232/) SQLMesh (https://sqlmesh.readthedocs.io/) Podcast Episode (https://www.dataengineeringpodcast.com/sqlmesh-open-source-dataops-episode-380) Airflow (https://airflow.apache.org/) Spark (https://spark.apache.org/) Flink (https://flink.apache.org/) Tabular (https://tabular.io/) Iceberg (https://iceberg.apache.org/) Open Policy Agent (https://www.openpolicyagent.org/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Unlocking Your dbt Projects With Practical Advice For Practitioners
Nov 20 2023
Unlocking Your dbt Projects With Practical Advice For Practitioners
Summary The dbt project has become overwhelmingly popular across analytics and data engineering teams. While it is easy to adopt, there are many potential pitfalls. Dustin Dorsey and Cameron Cyr co-authored a practical guide to building your dbt project. In this episode they share their hard-won wisdom about how to build and scale your dbt projects. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). That’s three free boards at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Dustin Dorsey and Cameron Cyr about how to design your dbt projects Interview Introduction How did you get involved in the area of data management? What was your path to adoption of dbt? What did you use prior to its existence? When/why/how did you start using it? What are some of the common challenges that teams experience when getting started with dbt? How does prior experience in analytics and/or software engineering impact those outcomes? You recently wrote a book to give a crash course in best practices for dbt. What motivated you to invest that time and effort? What new lessons did you learn about dbt in the process of writing the book? The introduction of dbt is largely responsible for catalyzing the growth of "analytics engineering". As practitioners in the space, what do you see as the net result of that trend? What are the lessons that we all need to invest in independent of the tool? For someone starting a new dbt project today, can you talk through the decisions that will be most critical for ensuring future success? As dbt projects scale, what are the elements of technical debt that are most likely to slow down engineers? What are the capabilities in the dbt framework that can be used to mitigate the effects of that debt? What tools or processes outside of dbt can help alleviate the incidental complexity of a large dbt project? What are the most interesting, innovative, or unexpected ways that you have seen dbt used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with dbt? (as engineers and/or as autors) What is on your personal wish-list for the future of dbt (or its competition?)? Contact Info Dustin LinkedIn (https://www.linkedin.com/in/dustindorsey/) Cameron LinkedIn (https://www.linkedin.com/in/cameron-cyr/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Biobot Analytic (https://biobot.io/) Breezeway (https://www.breezeway.io/) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) Synapse Analytics (https://azure.microsoft.com/en-us/products/synapse-analytics/) Snowflake (https://azure.microsoft.com/en-us/products/synapse-analytics/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) Analytics Power Hour (https://analyticshour.io/) DDL == Data Definition Language (https://en.wikipedia.org/wiki/Data_definition_language) DML == Data Manipulation Language (https://en.wikipedia.org/wiki/Data_manipulation_language) dbt codegen (https://github.com/dbt-labs/dbt-codegen) Unlocking dbt (https://amzn.to/49BhACq) book (affiliate link) dbt Mesh (https://www.getdbt.com/product/dbt-mesh) dbt Semantic Layer (https://www.getdbt.com/product/semantic-layer) GitHub Actions (https://github.com/features/actions) Metaplane (https://www.metaplane.dev/) Podcast Episode (https://www.dataengineeringpodcast.com/metaplane-data-observability-platform-episode-253/) DataTune Conference (https://www.datatuneconf.com/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine
Nov 13 2023
Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine
Summary Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Eran Yahav about building an AI powered developer assistant at Tabnine Interview Introduction How did you get involved in machine learning? Can you describe what Tabnine is and the story behind it? What are the individual and organizational motivations for using AI to generate code? What are the real-world limitations of generative AI for creating software? (e.g. size/complexity of the outputs, naming conventions, etc.) What are the elements of skepticism/oversight that developers need to exercise while using a system like Tabnine? What are some of the primary ways that developers interact with Tabnine during their development workflow? Are there any particular styles of software for which an AI is more appropriate/capable? (e.g. webapps vs. data pipelines vs. exploratory analysis, etc.) For natural languages there is a strong bias toward English in the current generation of LLMs. How does that translate into computer languages? (e.g. Python, Java, C++, etc.) Can you describe the structure and implementation of Tabnine? Do you rely primarily on a single core model, or do you have multiple models with subspecialization? How have the design and goals of the product changed since you first started working on it? What are the biggest challenges in building a custom LLM for code? What are the opportunities for specialization of the model architecture given the highly structured nature of the problem domain? For users of Tabnine, how do you assess/monitor the accuracy of recommendations? What are the feedback and reinforcement mechanisms for the model(s)? What are the most interesting, innovative, or unexpected ways that you have seen Tabnine's LLM powered coding assistant used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI assisted development at Tabnine? When is an AI developer assistant the wrong choice? What do you have planned for the future of Tabnine? Contact Info LinkedIn (https://www.linkedin.com/in/eranyahav/?originalSubdomain=il) Website (https://csaws.cs.technion.ac.il/~yahave/) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links TabNine (https://www.tabnine.com/) Technion University (https://www.technion.ac.il/en/home-2/) Program Synthesis (https://en.wikipedia.org/wiki/Program_synthesis) Context Stuffing (http://gptprompts.wikidot.com/context-stuffing) Elixir (https://elixir-lang.org/) Dependency Injection (https://en.wikipedia.org/wiki/Dependency_injection) COBOL (https://en.wikipedia.org/wiki/COBOL) Verilog (https://en.wikipedia.org/wiki/Verilog) MidJourney (https://www.midjourney.com/home) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)
Shining Some Light In The Black Box Of PostgreSQL Performance
Nov 6 2023
Shining Some Light In The Black Box Of PostgreSQL Performance
Summary Databases are the core of most applications, but they are often treated as inscrutable black boxes. When an application is slow, there is a good probability that the database needs some attention. In this episode Lukas Fittl shares some hard-won wisdom about the causes and solution of many performance bottlenecks and the work that he is doing to shine some light on PostgreSQL to make it easier to understand how to keep it running smoothly. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Your host is Tobias Macey and today I'm interviewing Lukas Fittl about optimizing your database performance and tips for tuning Postgres Interview Introduction How did you get involved in the area of data management? What are the different ways that database performance problems impact the business? What are the most common contributors to performance issues? What are the useful signals that indicate performance challenges in the database? For a given symptom, what are the steps that you recommend for determining the proximate cause? What are the potential negative impacts to be aware of when tuning the configuration of your database? How does the database engine influence the methods used to identify and resolve performance challenges? Most of the database engines that are in common use today have been around for decades. How have the lessons learned from running these systems over the years influenced the ways to think about designing new engines or evolving the ones we have today? What are the most interesting, innovative, or unexpected ways that you have seen to address database performance? What are the most interesting, unexpected, or challenging lessons that you have learned while working on databases? What are your goals for the future of database engines? Contact Info LinkedIn (https://www.linkedin.com/in/lfittl/) @LukasFittl (https://twitter.com/LukasFittl) on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links PGAnalyze (https://pganalyze.com/) Citus Data (https://www.citusdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/citus-data-with-ozgun-erdogan-and-craig-kerstiens-episode-13/) ORM == Object Relational Mapper (https://en.wikipedia.org/wiki/Object%E2%80%93relational_mapping) N+1 Query (https://docs.sentry.io/product/issues/issue-details/performance-issues/n-one-queries/) Autovacuum (https://www.postgresql.org/docs/current/routine-vacuuming.html#AUTOVACUUM) Write-ahead Log (https://en.wikipedia.org/wiki/Write-ahead_logging) pgstatio (https://pgpedia.info/p/pg_stat_io.html) randompagecost (https://postgresqlco.nf/doc/en/param/random_page_cost/) pgvector (https://github.com/pgvector/pgvector) Vector Database (https://en.wikipedia.org/wiki/Vector_database) Ottertune (https://ottertune.com/) Podcast Episode (https://www.dataengineeringpodcast.com/ottertune-database-performance-optimization-episode-197/) Citus Extension (https://github.com/citusdata/citus) Hydra (https://github.com/hydradatabase/hydra) Clickhouse (https://clickhouse.tech/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) MyISAM (https://en.wikipedia.org/wiki/MyISAM) MyRocks (http://myrocks.io/) InnoDB (https://en.wikipedia.org/wiki/InnoDB) Great Expectations (https://greatexpectations.io/) Podcast Episode (https://www.dataengineeringpodcast.com/great-expectations-data-contracts-episode-352) OpenTelemetry (https://opentelemetry.io/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Surveying The Market Of Database Products
Oct 30 2023
Surveying The Market Of Database Products
Summary Databases are the core of most applications, whether transactional or analytical. In recent years the selection of database products has exploded, making the critical decision of which engine(s) to use even more difficult. In this episode Tanya Bragin shares her experiences as a product manager for two major vendors and the lessons that she has learned about how teams should approach the process of tool selection. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). That’s three free boards at dataengineeringpodcast.com/miro (https://www.dataengineeringpodcast.com/miro). Your host is Tobias Macey and today I'm interviewing Tanya Bragin about her views on the database products market Interview Introduction How did you get involved in the area of data management? What are the aspects of the database market that keep you interested as a VP of product? How have your experiences at Elastic informed your current work at Clickhouse? What are the main product categories for databases today? What are the industry trends that have the most impact on the development and growth of different product categories? Which categories do you see growing the fastest? When a team is selecting a database technology for a given task, what are the types of questions that they should be asking? Transactional engines like Postgres, SQL Server, Oracle, etc. were long used as analytical databases as well. What is driving the broad adoption of columnar stores as a separate environment from transactional systems? What are the inefficiencies/complexities that this introduces? How can the database engine used for analytical systems work more closely with the transactional systems? When building analytical systems there are numerous moving parts with intricate dependencies. What is the role of the database in simplifying observability of these applications? What are the most interesting, innovative, or unexpected ways that you have seen Clickhouse used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on database products? What are your prodictions for the future of the database market? Contact Info LinkedIn (https://www.linkedin.com/in/tbragin/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Elastic (https://www.elastic.co/) OLAP (https://en.wikipedia.org/wiki/Online_analytical_processing) OLTP (https://en.wikipedia.org/wiki/Online_transaction_processing) Graph Database (https://en.wikipedia.org/wiki/Graph_database) Vector Database (https://en.wikipedia.org/wiki/Vector_database) Trino (https://trino.io/) Presto (https://prestodb.io/) Foreign data wrapper (https://wiki.postgresql.org/wiki/Foreign_data_wrappers) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) OpenTelemetry (https://opentelemetry.io/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363) Parquet (https://parquet.apache.org/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Defining A Strategy For Your Data Products
Oct 23 2023
Defining A Strategy For Your Data Products
Summary The primary application of data has moved beyond analytics. With the broader audience comes the need to present data in a more approachable format. This has led to the broad adoption of data products being the delivery mechanism for information. In this episode Ranjith Raghunath shares his thoughts on how to build a strategy for the development, delivery, and evolution of data products. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES (https://Neo4j.com/NODES). This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Your host is Tobias Macey and today I'm interviewing Ranjith Raghunath about tactical elements of a data product strategy Interview Introduction How did you get involved in the area of data management? Can you describe what is encompassed by the idea of a data product strategy? Which roles in an organization need to be involved in the planning and implementation of that strategy? order of operations: strategy -> platform design -> implementation/adoption platform implementation -> product strategy -> interface development managing grain of data in products team organization to support product development/deployment customer communications - what questions to ask? requirements gathering, helping to understand "the art of the possible" What are the most interesting, innovative, or unexpected ways that you have seen organizations approach data product strategies? What are the most interesting, unexpected, or challenging lessons that you have learned while working on defining and implementing data product strategies? When is a data product strategy overkill? What are some additional resources that you recommend for listeners to direct their thinking and learning about data product strategy? Contact Info LinkedIn (https://www.linkedin.com/in/ranjith-raghunath/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links CXData Labs (https://www.cxdatalabs.com/) Dimensional Modeling (https://en.wikipedia.org/wiki/Dimensional_modeling) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable
Oct 15 2023
Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable
Summary Building streaming applications has gotten substantially easier over the past several years. Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. Decodable was built with a mission of eliminating all of the painful aspects of developing and deploying stream processing systems for engineering teams. In this episode Eric Sammer discusses why more companies are including real-time capabilities in their products and the ways that Decodable makes it faster and easier. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES (https://Neo4j.com/NODES). Your host is Tobias Macey and today I'm interviewing Eric Sammer about starting your stream processing journey with Decodable Interview Introduction How did you get involved in the area of data management? Can you describe what Decodable is and the story behind it? What are the notable changes to the Decodable platform since we last spoke? (October 2021) What are the industry shifts that have influenced the product direction? What are the problems that customers are trying to solve when they come to Decodable? When you launched your focus was on SQL transformations of streaming data. What was the process for adding full Java support in addition to SQL? What are the developer experience challenges that are particular to working with streaming data? How have you worked to address that in the Decodable platform and interfaces? As you evolve the technical and product direction, what is your heuristic for balancing the unification of interfaces and system integration against the ability to swap different components or interfaces as new technologies are introduced? What are the most interesting, innovative, or unexpected ways that you have seen Decodable used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Decodable? When is Decodable the wrong choice? What do you have planned for the future of Decodable? Contact Info esammer (https://github.com/esammer) on GitHub LinkedIn (https://www.linkedin.com/in/esammer/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Decodable (https://www.decodable.co/) Podcast Episode (https://www.dataengineeringpodcast.com/decodable-streaming-data-pipelines-sql-episode-233/) Understanding the Apache Flink Journey (https://www.decodable.co/blog/understanding-the-apache-flink-journey?utm_source=podcast&utm_medium=paid&utm_campaign=data_engineering_podcast&utm_content=understanding_the_flink_journey) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57/) Debezium (https://debezium.io/) Podcast Episode (https://www.dataengineeringpodcast.com/debezium-change-data-capture-episode-114/) Kafka (https://kafka.apache.org/) Redpanda (https://redpanda.com/) Podcast Episode (https://www.dataengineeringpodcast.com/vectorized-red-panda-streaming-data-episode-152/) Kinesis (https://aws.amazon.com/kinesis/) PostgreSQL (https://www.postgresql.org/) Podcast Episode (https://www.dataengineeringpodcast.com/postgresql-with-jonathan-katz-episode-42/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Databricks (https://www.databricks.com/) Startree (https://startree.ai/) Pinot (https://pinot.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/pinot-embedded-analytics-episode-273/) Rockset (https://rockset.com/) Podcast Episode (https://www.dataengineeringpodcast.com/rockset-serverless-analytics-episode-101/) Druid (https://druid.apache.org/) InfluxDB (https://www.influxdata.com/) Samza (https://samza.apache.org/) Storm (https://storm.apache.org/) Pulsar (https://pulsar.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/pulsar-fast-and-scalable-messaging-with-rajan-dhabalia-and-matteo-merli-episode-17) ksqlDB (https://ksqldb.io/) Podcast Episode (https://www.dataengineeringpodcast.com/ksqldb-kafka-stream-processing-episode-122/) dbt (https://www.getdbt.com/) GitHub Actions (https://github.com/features/actions) Airbyte (https://airbyte.com/) Singer (https://www.singer.io/) Splunk (https://www.splunk.com/) Outbox Pattern (https://debezium.io/blog/2019/02/19/reliable-microservices-data-exchange-with-the-outbox-pattern/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)
Using Data To Illuminate The Intentionally Opaque Insurance Industry
Oct 9 2023
Using Data To Illuminate The Intentionally Opaque Insurance Industry
Summary The insurance industry is notoriously opaque and hard to navigate. Max Cho found that fact frustrating enough that he decided to build a business of making policy selection more navigable. In this episode he shares his journey of data collection and analysis and the challenges of automating an intentionally manual industry. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES (https://Neo4j.com/NODES). You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Max Cho about the wild world of insurance companies and the challenges of collecting quality data for this opaque industry Interview Introduction How did you get involved in the area of data management? Can you describe what CoverageCat is and the story behind it? What are the different sources of data that you work with? What are the most challenging aspects of collecting that data? Can you describe the formats and characteristics (3 Vs) of that data? What are some of the ways that the operational model of insurance companies have contributed to its opacity as an industry from a data perspective? Can you describe how you have architected your data platform? How have the design and goals changed since you first started working on it? What are you optimizing for in your selection and implementation process? What are the sharp edges/weak points that you worry about in your existing data flows? How do you guard against those flaws in your day-to-day operations? What are the most interesting, innovative, or unexpected ways that you have seen your data sets used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on insurance industry data? When is a purely statistical view of insurance the wrong approach? What do you have planned for the future of CoverageCat's data stack? Contact Info LinkedIn (https://www.linkedin.com/in/maxrcho/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links CoverageCat (https://www.coveragecat.com/) Actuarial Model (https://en.wikipedia.org/wiki/Actuarial_science) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)