Summary A significant portion of the time spent by data engineering teams is on managing the workflows and operations of their pipelines. DataOps has arisen as a parallel set of practices to that of DevOps teams as a means of reducing wasted effort. Agile Data Engine is a platform designed to handle the infrastructure side of the DataOps equation, as well as providing the insights that you need to manage the human side of the workflow. In this episode Tevje Olin explains how the platform is implemented, the features that it provides to reduce the amount of effort required to keep your pipelines running, and how you can start using it in your own team. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing Tevje Olin about Agile Data Engine, a platform that combines data modeling, transformations, continuous delivery and workload orchestration to help you manage your data products and the whole lifecycle of your warehouse Interview Introduction How did you get involved in the area of data management? Can you describe what Agile Data Engine is and the story behind it? What are some of the tools and architectures that an organization might be able to replace with Agile Data Engine? How does the unified experience of Agile Data Engine change the way that teams think about the lifecycle of their data? What are some of the types of experiments that are enabled by reduced operational overhead? What does CI/CD look like for a data warehouse? How is it different from CI/CD for software applications? Can you describe how Agile Data Engine is architected? How have the design and goals of the system changed since you first started working on it? What are the components that you needed to develop in-house to enable your platform goals? What are the changes in the broader data ecosystem that have had the most influence on your product goals and customer adoption? Can you describe the workflow for a team that is using Agile Data Engine to power their business analytics? What are some of the insights that you generate to help your customers understand how to improve their processes or identify new opportunities? In your "about" page it mentions the unique approaches that you take for warehouse automation. How do your practices differ from the rest of the industry? How have changes in the adoption/implementation of ML and AI impacted the ways that your customers exercise your platform? What are the most interesting, innovative, or unexpected ways that you have seen the Agile Data Engine platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Agile Data Engine? When is Agile Data Engine the wrong choice? What do you have planned for the future of Agile Data Engine? Guest Contact Info LinkedIn (https://www.linkedin.com/in/tevjeolin/?originalSubdomain=fi) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? About Agile Data Engine Agile Data Engine unlocks the potential of your data to drive business value - in a rapidly changing world. Agile Data Engine is a DataOps Management platform for designing, deploying, operating and managing data products, and managing the whole lifecycle of a data warehouse. It combines data modeling, transformations, continuous delivery and workload orchestration into the same platform. Links Agile Data Engine (https://www.agiledataengine.com/agile-data-engine-x-data-engineering-podcast) Bill Inmon (https://en.wikipedia.org/wiki/Bill_Inmon) Ralph Kimball (https://en.wikipedia.org/wiki/Ralph_Kimball) Snowflake (https://www.snowflake.com/en/) Redshift (https://aws.amazon.com/redshift/) BigQuery (https://cloud.google.com/bigquery) Azure Synapse (https://azure.microsoft.com/en-us/products/synapse-analytics/) Airflow (https://airflow.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/)

Summary Building a data team is hard in any circumstance, but at a startup it can be even more challenging. The requirements are fluid, you probably don't have a lot of existing data talent to manage the hiring and onboarding, and there is a need to move fast. Ghalib Suleiman has been on both sides of this equation and joins the show to share his hard-won wisdom about how to start and grow a data team in the early days of company growth. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing Ghalib Suleiman about challenges and strategies for building data teams in a startup Interview Introduction How did you get involved in the area of data management? Can you start by sharing your conception of the responsibilities of a data team? What are some of the common fallacies that organizations fall prey to in their first efforts at building data capabilities? Have you found it more practical to hire outside talent to build out the first data systems, or grow that talent internally? What are some of the resources you have found most helpful in training/educating the early creators and consumers of data assets? When there is no internal data talent to assist with hiring, what are some of the problems that manifest in the hiring process? What are the concepts that the new hire needs to know? How much does the hiring manager/interviewer need to know about those concepts to evaluate skill? What are the most critical skills for a first hire to have to start generating valuable output? As a solo data person, what are the uphill battles that they need to be prepared for in the organization? What are the rabbit holes that they should beware of? What are some of the tactical What are the most interesting, innovative, or unexpected ways that you have seen initial data hires tackle startup challenges? What are the most interesting, unexpected, or challenging lessons that you have learned while working on starting and growing data teams? When is it more practical to outsource the data work? Contact Info LinkedIn (https://www.linkedin.com/in/ghalibs/) @ghalib (https://twitter.com/ghalib) 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 [email protected] (mailto:[email protected])) 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 Polytomic (https://www.polytomic.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/)

Summary Batch vs. streaming is a long running debate in the world of data integration and transformation. Proponents of the streaming paradigm argue that stream processing engines can easily handle batched workloads, but the reverse isn't true. The batch world has been the default for years because of the complexities of running a reliable streaming system at scale. In order to remove that barrier, the team at Estuary have built the Gazette and Flow systems from the ground up to resolve the pain points of other streaming engines, while providing an intuitive interface for data and application engineers to build their streaming workflows. In this episode David Yaffe and Johnny Graettinger share the story behind the business and technology and how you can start using it today to build a real-time data lake without all of the headache. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing David Yaffe and Johnny Graettinger about using streaming data to build a real-time data lake and how Estuary gives you a single path to integrating and transforming your various sources Interview Introduction How did you get involved in the area of data management? Can you describe what Estuary is and the story behind it? Stream processing technologies have been around for around a decade. How would you characterize the current state of the ecosystem? What was missing in the ecosystem of streaming engines that motivated you to create a new one from scratch? With the growth in tools that are focused on batch-oriented data integration and transformation, what are the reasons that an organization should still invest in streaming? What is the comparative level of difficulty and support for these disparate paradigms? What is the impact of continuous data flows on dags/orchestration of transforms? What role do modern table formats have on the viability of real-time data lakes? Can you describe the architecture of your Flow platform? What are the core capabilities that you are optimizing for in its design? What is involved in getting Flow/Estuary deployed and integrated with an organization's data systems? What does the workflow look like for a team using Estuary? How does it impact the overall system architecture for a data platform as compared to other prevalent paradigms? How do you manage the translation of poll vs. push availability and best practices for API and other non-CDC sources? What are the most interesting, innovative, or unexpected ways that you have seen Estuary used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Estuary? When is Estuary the wrong choice? What do you have planned for the future of Estuary? Contact Info Dave Y (mailto:[email protected]) Johnny G (mailto:[email protected]) 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 [email protected] (mailto:[email protected])) 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 Estuary (https://estuary.dev) Try Flow Free (https://dashboard.estuary.dev/register) Gazette (https://gazette.dev) Samza (https://samza.apache.org/) Flink (https://flink.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/apache-flink-with-fabian-hueske-episode-57/) Storm (https://storm.apache.org/) Kafka Topic Partitioning (https://www.openlogic.com/blog/kafka-partitions) Trino (https://trino.io/) Avro (https://avro.apache.org/) Parquet (https://parquet.apache.org/) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) Airbyte (https://www.dataengineeringpodcast.com/airbyte-open-source-data-integration-episode-173/) Snowflake (https://www.snowflake.com/en/) BigQuery (https://cloud.google.com/bigquery) Vector Database (https://learn.microsoft.com/en-us/semantic-kernel/concepts-ai/vectordb) CDC == Change Data Capture (https://en.wikipedia.org/wiki/Change_data_capture) Debezium (https://debezium.io/) Podcast Episode (https://www.dataengineeringpodcast.com/debezium-change-data-capture-episode-114/) MapReduce (https://en.wikipedia.org/wiki/MapReduce) Netflix DBLog (https://netflixtechblog.com/dblog-a-generic-change-data-capture-framework-69351fb9099b) JSON-Schema (http://json-schema.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/)

Summary All of the advancements in our technology is based around the principles of abstraction. These are valuable until they break down, which is an inevitable occurrence. In this episode the host Tobias Macey shares his reflections on recent experiences where the abstractions leaked and some observances on how to deal with that situation in a data platform architecture. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm sharing some thoughts and observances about abstractions and impedance mismatches from my experience building a data lakehouse with an ELT workflow Interview Introduction impact of community tech debt hive metastore new work being done but not widely adopted tensions between automation and correctness data type mapping integer types complex types naming things (keys/column names from APIs to databases) disaggregated databases - pros and cons flexibility and cost control not as much tooling invested vs. Snowflake/BigQuery/Redshift data modeling dimensional modeling vs. answering today's questions What are the most interesting, unexpected, or challenging lessons that you have learned while working on your data platform? When is ELT the wrong choice? What do you have planned for the future of your data platform? Contact Info LinkedIn (https://www.linkedin.com/in/tmacey/) 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 [email protected] (mailto:[email protected])) 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 dbt (https://www.getdbt.com/) Airbyte (https://airbyte.com/) Podcast Episode (https://www.dataengineeringpodcast.com/airbyte-open-source-data-integration-episode-173/) Dagster (https://dagster.io/) Podcast Episode (https://www.dataengineeringpodcast.com/dagster-software-defined-assets-data-orchestration-episode-309/) Trino (https://trino.io/) Podcast Episode (https://www.dataengineeringpodcast.com/presto-distributed-sql-episode-149/) ELT (https://en.wikipedia.org/wiki/Extract,_load,_transform) Data Lakehouse (https://www.forbes.com/sites/bernardmarr/2022/01/18/what-is-a-data-lakehouse-a-super-simple-explanation-for-anyone/?sh=5c0e333f6088) Snowflake (https://www.snowflake.com/en/) BigQuery (https://cloud.google.com/bigquery) Redshift (https://aws.amazon.com/redshift/) Technical Debt (https://en.wikipedia.org/wiki/Technical_debt) Hive Metastore (https://cwiki.apache.org/confluence/display/Hive/AdminManual+Metastore+Administration) AWS Glue (https://aws.amazon.com/glue/) 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/)

Summary Every business has customers, and a critical element of success is understanding who they are and how they are using the companies products or services. The challenge is that most companies have a multitude of systems that contain fragments of the customer's interactions and stitching that together is complex and time consuming. Segment created the Unify product to reduce the burden of building a comprehensive view of customers and synchronizing it to all of the systems that need it. In this episode Kevin Niparko and Hanhan Wang share the details of how it is implemented and how you can use it to build and maintain rich customer profiles. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing Kevin Niparko and Hanhan Wang about Segment's new Unify product for building and syncing comprehensive customer profiles across your data systems Interview Introduction How did you get involved in the area of data management? Can you describe what Segment Unify is and the story behind it? What are the net-new capabilities that it brings to the Segment product suite? What are some of the categories of attributes that need to be managed in a prototypical customer profile? What are the different use cases that are enabled/simplified by the availability of a comprehensive customer profile? What is the potential impact of more detailed customer profiles on LTV? How do you manage permissions/auditability of updating or amending profile data? Can you describe how the Unify product is implemented? What are the technical challenges that you had to address while developing/launching this product? What is the workflow for a team who is adopting the Unify product? What are the other Segment products that need to be in use to take advantage of Unify? What are some of the most complex edge cases to address in identity resolution? How does reverse ETL factor into the enrichment process for profile data? What are some of the issues that you have to account for in synchronizing profiles across platforms/products? How do you mititgate the impact of "regression to the mean" for systems that don't support all of the attributes that you want to maintain in a profile record? What are some of the data modeling considerations that you have had to account for to support e.g. historical changes (e.g. slowly changing dimensions)? What are the most interesting, innovative, or unexpected ways that you have seen Segment Unify used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Segment Unify? When is Segment Unify the wrong choice? What do you have planned for the future of Segment Unify? Contact Info Kevin LinkedIn (https://www.linkedin.com/in/kevin-niparko-5ab86b54/) Blog (https://n2parko.com/) Hanhan LinkedIn (https://www.linkedin.com/in/hansquared/) 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 [email protected] (mailto:[email protected])) 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 Segment Unify (https://segment.com/product/unify/) Segment (https://segment.com/) Podcast Episode (https://www.dataengineeringpodcast.com/segment-customer-analytics-episode-72/) Customer Data Platform (CDP) (https://blog.hubspot.com/service/customer-data-platform-guide) Golden Profile (https://www.uniserv.com/en/business-cases/customer-data-management/golden-record-golden-profile/) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) MarTech Landscape (https://chiefmartec.com/2023/05/2023-marketing-technology-landscape-supergraphic-11038-solutions-searchable-on-martechmap-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/)