Oz Katz

A data lake is primarily two things: an object store and the objects being stored. Even with the most basic setup, data lakes are capable of supporting BI, Machine Learning, and operational analytics use cases. This flexibility speaks to the strength of object stores, particularly their flexibility in integrating with a diverse set of data processing engines.

As data lakes exploded in adoption, a number of improvements were made to the first architectures.

Even newer improvements have been the emergence of data source control tools that bring new levels of manageability across an entire lake! In this talk, we’ll cover how to incorporate these open technologies into your data lake, and how they simplify workflows critical to ML experimentation, deployment of datasets, and more!