Add Version Control To Your Data Lake With LakeFS
Data Engineering Podcast
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Exploring Version Control in Data Management
The chapter features a discussion on the speakers' professional experiences and the increasing complexities of managing large datasets. It introduces the concept of version control in data lakes, paving the way for an exploration of the LakeFS system created by Treeverse.
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Summary
Data lakes are gaining popularity due to their flexibility and reduced cost of storage. Along with the benefits there are some additional complexities to consider, including how to safely integrate new data sources or test out changes to existing pipelines. In order to address these challenges the team at Treeverse created LakeFS to introduce version control capabilities to your storage layer. In this episode Einat Orr and Oz Katz explain how they implemented branching and merging capabilities for object storage, best practices for how to use versioning primitives to introduce changes to your data lake, how LakeFS is architected, and how you can start using it for your own data platform.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Einat Orr and Oz Katz about their work at Treeverse on the LakeFS system for versioning your data lakes the same way you version your code.
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by giving an overview of what LakeFS is and why you built it?
- There are a number of tools and platforms that support data virtualization and data versioning. How does LakeFS compare to the available options? (e.g. Alluxio, Denodo, Pachyderm, DVC, etc.)
- What are the primary use cases that LakeFS enables?
- For someone who wants to use LakeFS what is involved in getting it set up?
- How is LakeFS implemented?
- How has the design of the system changed or evolved since you began working on it?
- What assumptions did you have going into it which have since been invalidated or modified?
- How does the workflow for an engineer or analyst change from working directly against S3 to running against the LakeFS interface?
- How do you handle merge conflicts and resolution?
- What are some of the potential edge cases or foot guns that they should be aware of when there are multiple people using the same repository?
- How do you approach management of the data lifecycle or garbage collection to avoid ballooning the cost of storage for a dataset that is tracking a high number of branches with diverging commits?
- Given that S3 and GCS are eventually consistent storage layers, how do you handle snapshots/transactionality of the data you are working with?
- What are the axes for scaling an installation of LakeFS?
- What are the limitations in terms of size or geographic distribution of the datasets?
- What are some of the most interesting, unexpected, or innovative ways that you have seen LakeFS being used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while building LakeFS?
- When is LakeFS the wrong choice?
- What do you have planned for the future of the project?
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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links
- Treeverse
- LakeFS
- lakeFS Slack Channel
- SimilarWeb
- Kaggle
- DagsHub
- Alluxio
- Pachyderm
- DVC
- ML Ops (Machine Learning Operations)
- DoltHub
- Delta Lake
- Hudi
- Iceberg Table Format
- Kubernetes
- PostgreSQL
- Git
- Spark
- Presto
- CockroachDB
- YugabyteDB
- Citus
- Hive Metastore
- Iceberg Table Format
- Immunai
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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