Latest Articles

Generative AI feature summarizes query-level context for data discovery and development workflows in Stemma's data catalog

Three queries and simple steps that data engineers can use to reduce Snowflake compute costs due to ETL

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By looking closely at the way dbt materializes tables we can better understand how to track lineage through SQL queries.

A data team’s success hinges on its leader’s soft skills but, this is easy to lose sight of given the backlog of technical work. These two tips, earned from experience, can help new managers keep a focus on the fuzzy but important work of managing across teams.

A look at how Data leaders are facing a lack of resources, the changing role of data engineers, and the challenge of self-serve

In part 2 of Mark's conversation with Chad Sanderson about the Semantic Data Warehouse, they discuss the importance of semantically defining entities and new roles for contracts and the data catalog.

In this first part of Mark's discussion with Chad Sanford, they focus on "semantics," how it should be defined, and how the Semantic Data Warehouse fits with the modern data stack

Lessons from SaaS on why open standards may not be the answer for solving the metadata problem

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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.