Summary Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service. 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 is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. 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 Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou Interview Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution? What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges? How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine? What are the challenges in terms of safety and reliability? What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics? Contact Info LinkedIn (https://www.linkedin.com/in/diptiborkar/) 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 Microsoft Fabric (https://www.microsoft.com/microsoft-fabric) Ahana episode (https://www.dataengineeringpodcast.com/ahana-presto-cloud-data-lake-episode-217) DB2 Distributed (https://www.ibm.com/docs/en/db2/11.5?topic=managers-designing-distributed-databases) Spark (https://spark.apache.org/) Presto (https://prestodb.io/) Azure Data (https://azure.microsoft.com/en-us/products#analytics) MAD Landscape (https://mattturck.com/mad2024/) Podcast Episode (https://www.dataengineeringpodcast.com/mad-landscape-2023-data-infrastructure-episode-369) ML Podcast Episode (https://www.themachinelearningpodcast.com/mad-landscape-2023-ml-ai-episode-21) Tableau (https://www.tableau.com/) dbt (https://www.getdbt.com/) Medallion Architecture (https://dataengineering.wiki/Concepts/Medallion+Architecture) Microsoft Onelake (https://learn.microsoft.com/fabric/onelake/onelake-overview) ORC (https://orc.apache.org/) Parquet (https://parquet.incubator.apache.org) Avro (https://avro.apache.org/) Delta Lake (https://delta.io/) 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) Hadoop (https://hadoop.apache.org/) PowerBI (https://www.microsoft.com/power-platform/products/power-bi) Podcast Episode (https://www.dataengineeringpodcast.com/power-bi-business-intelligence-episode-154) Velox (https://velox-lib.io/) Gluten (https://gluten.apache.org/) Apache ...
