Listen to this episode from Data Engineering Podcast on Spotify. Summary Data systems are inherently complex and often require integration of multiple technologies. Orchestrators are centralized utilities that control the execution and sequencing of interdependent operations. This offers a single location for managing visibility and error handling so that data platform engineers can manage complexity. In this episode Nick Schrock, creator of Dagster, shares his perspective on the state of data orchestration technology and its application to help inform its implementation in your environment. 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. 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Your host is Tobias Macey and today I'm welcoming back Nick Schrock to talk about the state of the ecosystem for data orchestration Interview Introduction How did you get involved in the area of data management? Can you start by defining what data orchestration is and how it differs from other types of orchestration systems? (e.g. container orchestration, generalized workflow orchestration, etc.) What are the misconceptions about the applications of/need for/cost to implement data orchestration? How do those challenges of customer education change across roles/personas? Because of the multi-faceted nature of data in an organization, how does that influence the capabilities and interfaces that are needed in an orchestration engine? You have been working on Dagster for five years now. How have the requirements/adoption/application for orchestrators changed in that time? One of the challenges for any orchestration engine is to balance the need for robust and extensible core capabilities with a rich suite of integrations to the broader data ecosystem. What are the factors that you have seen make the most influence in driving adoption of a given engine? What are the most interesting, innovative, or unexpected ways that you have seen data orchestration implemented and/or used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration? When is a data orchestrator the wrong choice? What do you have planned for the future of orchestration with Dagster? Contact Info @schrockn (https://twitter.com/schrockn) on Twitter LinkedIn (https://www.linkedin.com/in/schrockn) 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 Dagster (https://dagster.io/) GraphQL (https://graphql.org/) K8s == Kubernetes (https://kubernetes.io/) Airbyte (https://airbyte.com/) Podcast Episode (https://www.dataengineeringpodcast.com/airbyte-open-source-data-integration-episode-173/) Hightouch (https://hightouch.com/) Podcast Episode (https://www.dataengineeringpodcast.com/hightouch-customer-data-warehouse-episode-168/) Airflow (https://airflow.apache.org/) Prefect (https://www.prefect.io) Flyte (https://flyte.org/) Podcast Episode (https://www.dataengineeringpodcast.com/flyte-data-orchestration-machine-learning-episode-291/) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) DAG == Directed Acyclic Graph (https://en.wikipedia.org/wiki/Directed_acyclic_graph) Temporal (https://temporal.io/) Software Defined Assets (https://docs.dagster.io/concepts/assets/software-defined-assets) DataForm (https://dataform.co/) Gradient Flow State Of Orchestration Report 2022 (https://gradientflow.com/2022-workflow-orchestration-survey/) MLOps Is 98% Data Engineering (https://mlops.community/mlops-is-mostly-data-engineering/) DataHub (https://datahubproject.io/) Podcast Episode (https://www.dataengineeringpodcast.com/datahub-metadata-management-episode-147/) OpenMetadata (https://open-metadata.org/) Podcast Episode (https://www.dataengineeringpodcast.com/openmetadata-universal-metadata-layer-episode-237/) Atlan (https://atlan.com/) Podcast Episode (https://www.dataengineeringpodcast.com/atlan-data-team-collaboration-episode-179/) 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/)
