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Data Engineering Podcast
Version Your Data Lakehouse Like Your Software With Nessie
Mar 10, 2024
Learn how the Nessie project bridges data lake and warehouse capabilities with versioning semantics similar to Git. Explore effective versioning and branching strategies, architecture of Nessie, and future development plans. Discover the advantages of using Nessie for data versioning in multi-table transactions within a data lakehouse setting.
40:55
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Quick takeaways
- Nessie provides Git-like versioning for data lakes, enabling disaster recovery and new data ops practices.
- Nessie simplifies data rollback and branch environments, streamlining testing and reducing storage costs.
Deep dives
Nessie Project Overview
Nessie is a Git-like versioned catalog for data lakes using Apache Iceberg. It provides branching and commit capabilities at the catalog level, enabling Git-like semantics for data ops practices such as disaster recovery. Nessie's key feature is creating branches and commits at the catalog level, changing how developers interact with the data and facilitating new data lake house patterns.
Versioning and Branching Benefits
Nessie allows for easy data rollback by rolling back entire catalogs to clean commits, streamlining the process compared to rolling back individual tables. It enables creating isolated branch environments without duplicating existing data, reducing storage costs. The branching and merging options help manage testing and experimentation scenarios without impacting the main production data, simplifying stress testing and worst-case scenario simulations.
Integration and Deployment
For deployment, there are Docker containers for testing and Helm charts for production deployment. Catalog migration is simplified with the CLI tool for copying references between catalogs, aiding in transitioning existing setups to work with Nessie. When integrated with tools like Dremio, Nessie's versioning and branching primitives can be leveraged seamlessly into existing data workflows, such as in SQL queries.
Future Development and Wishlist
Future plans for Nessie include enhancing context awareness to facilitate more sophisticated merges and adding high Iceberg support to expand compatibility. The aim is to make Nesse a versatile catalog tool that can support different table formats beyond Iceberg. The collaborative environment on the Nessie project and the open communication channels like Zulip allow for community participation in shaping and advancing the project's roadmap.
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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- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And 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? 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.
- 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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