Latest Articles

The notes provided by Meta's privacy engineers provide excellent insight into how to manage data policies.

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Learn the 3 crucial steps that great data engineering teams follow for a successful data migration.

One of the early questions that data engineering teams pose when implementing a catalog is: should we make the catalog responsible for gathering metadata from data systems ("pull"), or task data systems with reporting metadata to the catalog ("push")? And, what are the consequences of using one approach over the other? Learn how to ingest metadata into your catalog and which method to choose.

Why is data discovery important? What is the role for data discovery in data mesh? Who's responsible for making data discoverable? Learn the answers to these questions (and more!) — summarized from a recent panel discussion on Data Discovery in Data Mesh.

Has anyone ever asked you, "Who's the data owner?" Learn how to define data ownership and how a data catalog can help you document this information.

It’s time for the data catalog to evolve. Catalogs already have access to rich, cross-sectional views of your data ecosystem. The next frontier is to repurpose this information for operational use by integrating your catalog into your CICD pipeline.

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Here are the top 2 reasons why data catalogs end up failing — and how to avoid them.

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Learn why adoption and customer satisfaction are the top 2 goals that matter when it comes to measuring the success of your data catalog.