Stitching Together Enterprise Analytics With Microsoft Fabric
Data Engineering Podcast
Integration of Native Spark Engine and X-Table for Seamless Interop
The chapter explores the integration of Spark engine with gluten and Velox, showcasing reduced CPU usage and performance improvements in benchmarks like TPC-H and TPC-DS. It highlights the X-Table set of operators for any engine and emphasizes seamless interop with Apache X-Table, showcasing collaborations with Onehouse, GCP, and GSL Labs.
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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 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
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__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site 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 with your story.
Links
- Microsoft Fabric
- Ahana episode
- DB2 Distributed
- Spark
- Presto
- Azure Data
- MAD Landscape
- Tableau
- dbt
- Medallion Architecture
- Microsoft Onelake
- ORC
- Parquet
- Avro
- Delta Lake
- Iceberg
- Hudi
- Hadoop
- PowerBI
- Velox
- Gluten
- Apache XTable
- GraphQL
- Formula 1
- McLaren
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Starburst:  This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, the query engine Apache Iceberg was designed for, Starburst is an open platform with support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. Go to [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
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