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Intro: The challenges in managing a data lake for structured and unstructured data.

Achieving manageability: 1. The components of the architecture and their role Opentable formats Catalogs Data Version control systems 2. How it all fits together Example using Databricks technologies Example using Apache Iceberg Example using AWS technologies 3. Discussion

Language: English

About the lecturer: Einat Orr is the CEO and Co-founder of Treeverse, the company behind lakeFS, an open source platform that delivers a git-like experience to object-storage based data lakes. She received her PhD. in Mathematics from Tel Aviv University, in the field of optimization in graph theory. Einat previously led several engineering organizations, most recently as CTO at SimilarWeb.

Intro: The challenges in managing a data lake for structured and unstructured data.

Achieving manageability: 1. The components of the architecture and their role Opentable formats Catalogs Data Version control systems 2. How it all fits together Example using Databricks technologies Example using Apache Iceberg Example using AWS technologies 3. Discussion

Language: English

About the lecturer: Einat Orr is the CEO and Co-founder of Treeverse, the company behind lakeFS, an open source platform that delivers a git-like experience to object-storage based data lakes. She received her PhD. in Mathematics from Tel Aviv University, in the field of optimization in graph theory. Einat previously led several engineering organizations, most recently as CTO at SimilarWeb.

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

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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?

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