Version Your Data Lakehouse Like Your Software With Nessie
Data Engineering Podcast
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Comparing NESSI and Lake FS Data Ecosystems
When comparing NESSI and Lake FS in data ecosystems, NESSI focuses on capturing metadata changes while Lake FS captures deltas in actual files. NESSI is more suitable for capturing metadata changes, like when an iceberg table is updated with an insert, resulting in creating multiple new files. On the other hand, Lake FS captures deltas in the actual files by adding and subtracting files to reflect changes in the data. Both projects emerged around the same time and initially aimed to use 'get' semantics but realized the need for different abstractions due to the nature and volume of data changes. NESSI opts for metadata change capture, while Lake FS takes the approach of file delta capture.
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Summary
Data lakehouse architectures are gaining popularity due to the flexibility and cost effectiveness that they offer. The link that bridges the gap between data lake and warehouse capabilities is the catalog. The primary purpose of the catalog is to inform the query engine of what data exists and where, but the Nessie project aims to go beyond that simple utility. In this episode Alex Merced explains how the branching and merging functionality in Nessie allows you to use the same versioning semantics for your data lakehouse that you are used to from Git.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council and use code dataengpod20 to register today!
- Your host is Tobias Macey and today I'm interviewing Alex Merced, developer advocate at Dremio and co-author of the upcoming book from O'reilly, "Apache Iceberg, The definitive Guide", about Nessie, a git-like versioned catalog for data lakes using Apache Iceberg
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Nessie is and the story behind it?
- What are the core problems/complexities that Nessie is designed to solve?
- The closest analogue to Nessie that I've seen in the ecosystem is LakeFS. What are the features that would lead someone to choose one or the other for a given use case?
- Why would someone choose Nessie over native table-level branching in the Apache Iceberg spec?
- How do the versioning capabilities compare to/augment the data versioning in Iceberg?
- What are some of the sources of, and challenges in resolving, merge conflicts between table branches?
- Can you describe the architecture of Nessie?
- How have the design and goals of the project changed since it was first created?
- What is involved in integrating Nessie into a given data stack?
- For cases where a given query/compute engine doesn't natively support Nessie, what are the options for using it effectively?
- How does the inclusion of Nessie in a data lake influence the overall workflow of developing/deploying/evolving processing flows?
- What are the most interesting, innovative, or unexpected ways that you have seen Nessie used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working with Nessie?
- When is Nessie the wrong choice?
- What have you heard is planned for the future of Nessie?
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
- Project Nessie
- Article: What is Nessie, Catalog Versioning and Git-for-Data?
- Article: What is Lakehouse Management?: Git-for-Data, Automated Apache Iceberg Table Maintenance and more
- Free Early Release Copy of "Apache Iceberg: The Definitive Guide"
- Iceberg
- Arrow
- Data Lakehouse
- LakeFS
- AWS Glue
- Tabular
- Trino
- Presto
- Dremio
- RocksDB
- Delta Lake
- Hive Metastore
- PyIceberg
- Optimistic Concurrency Control
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 - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. 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. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. 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. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Data Council:  Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit [dataengineeringpodcast.com/data-council](https://www.dataengineeringpodcast.com/data-council) and use code **dataengpod20** to register today! Promo Code: dataengpod20
- Dagster:  Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage. Dagster is an open-source orchestration solution that helps data teams reign in this complexity and build data platforms that provide unparalleled observability, and testability, all while fostering collaboration across the enterprise. With enterprise-grade hosting on Dagster Cloud, you gain even more capabilities, adding cost management, security, and CI support to further boost your teams' productivity. Go to [dagster.io](https://dagster.io/lp/dagster-cloud-trial?source=data-eng-podcast) today to get your first 30 days free!
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