An Exploration Of The Open Data Lakehouse And Dremio's Contribution To The Ecosystem

Summary The "data lakehouse" architecture balances the scalability and flexibility of data lakes with the ease of use and transaction support of data warehouses. Dremio is one of the companies leading the development of products and services that support the open lakehouse. In this episode Jason Hughes explains what it means for a lakehouse to be "open" and describes the different components that the Dremio team build and contribute to. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. 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Your host is Tobias Macey and today I’m interviewing Jason Hughes about the work that Dremio is doing to support the open lakehouse Interview Introduction How did you get involved in the area of data management? Can you describe what Dremio is and the story behind it? What are some of the notable changes in the Dremio product and related ecosystem over the past ~4 years? How has the advent of the lakehouse paradigm influenced the product direction? What are the main benefits that a lakehouse design offers to a data platform? What are some of the architectural patterns that are only possible with a lakehouse? What is the distinction you make between a lakehouse and an open lakehouse? What are some of the unique features that Dremio offers for lakehouse implementations? What are some of the investments that Dremio has made to the broader open source/open lakehouse ecosystem? How are those projects/investments being used in the commercial offering? What is the purchase/usage model that customers expect for lakehouse implementations? How have those expectations shifted since the first iterations of Dremio? Dremio has its ancestry in the Drill project. How has that history influenced the capabilities (e.g. integrations, scalability, deployment models, etc.) and evolution of Dremio compared to systems like Trino/Presto and Spark SQL? What are the most interesting, innovative, or unexpected ways that you have seen Dremio used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dremio? When is Dremio the wrong choice? What do you have planned for the future of Dremio? Contact Info Email LinkedIn 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. 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To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Dremio Podcast Episode Dremio Sonar Dremio Arctic DML == Data Modification Language Spark Data Lake Trino Presto Dremio Data Reflections Tableau Delta Lake Podcast Episode Apache Impala Apache Arrow DuckDB Podcast Episode Google BigLake Project Nessie Apache Iceberg Podcast Episode Hive Metastore AWS Glue Catalog Dremel Apache Drill Arrow Gandiva dbt Airbyte Podcast Episode Singer The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast