Latest Articles

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

The most current matchup on Stemma v. Amundsen; how Stemma provides a friendlier experience for analysts and business users, improves productivity through lineage, and simplifies catalog setup and administration.

In part 2, Stemma's Head of Design, Dani Sandoval, continues his deep dive into the changes made to the data catalog interface to decrease frustration when debugging data issues, help data engineers establish best practices within data teams, and make discovering existing data easier for data scientists and analysts.

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Stemma's Head of Design, Dani Sandoval, goes deep into the changes made to the data catalog interface to decrease frustration when debugging data issues, help data engineers establish best practices within data teams, and make discovering existing data easier for data scientists and analysts.

Stemma releases the Data Developer Update to further improve the way dbt users manage their data development lifecycle: include the Lineage Graph for complex environments, dbt Cloud integration, and column Autodescribe to pull documentation based on column-level lineage

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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.

We define micro migrations in data engineering, provide some real world examples, and then give three patterns you can employ manage them better

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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A deeper look at how to balance proactive and reactive methods for successful data management

The team at Stemma developed tools to automate data stewardship for companies who won by making big bets on data. They are now building tools for modern, democratized teams.

Data management is best handled by balancing proactive and reactive methods

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

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1) Business Glossary
2) View columns in search results

Stemma and Tableau announce partnership to help shared customers build better dashboards.

March Updates:
1) seeing why a search result is being displayed
2) ability to conduct a single search for multiple asset types
3) authentication improvements for previewing BigQuery data
4) updated MySQL integration.

Learn how Stemma is different from Amundsen

Building dashboards that help you make better decisions using Tableau and Stemma

February Updates:
1) Create shared descriptions with exact matching columns
2) Create shared descriptions with exact matching columns
3) Delta Lake integration

Learn how to migrate from open-source Amundsen to Stemma based on organizations like iRobot and more.

How to effectively define ownership of your data assets while onboarding a data catalog

Our SOC 2 Type II certification is a publicly visible milestone in the journey towards securing the privacy of your data. In this post, we discuss what this step in Stemma's journey means for our existing and future customers.

The data catalog is becoming a ubiquitous part of the data ecosystem, so much so that the new data stack is now known by its three pillars: the warehouse, the BI tool and the data catalog. However, many organizations still struggle to wrangle their data in a way that allows users to answer the most basic questions: what data exists and what does it mean?

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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This step-by-step guide explains how to enable Open ID Connect (OIDC) for your Amundsen installation.

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

Today, we are excited to announce the launch of Stemma - a fully managed data catalog, powered by Amundsen, the leading open-source data catalog with the largest community and broadest adoption. We raised $4.8M in seed funding led by Sequoia to bring the power of the leading open-source data catalog to every organization.

Almost any organization using Amundsen will need to make custom changes to their install. Unfortunately, this has been a long-time issue for the community. This post is the first in a step-by-step guide to getting a fully customized enterprise deployment of Amundsen¹, based on how Stemma deploys Amundsen for its customers.

All recent innovation in data has taken place in two areas — helping data engineers produce data, and helping data consumers (primarily data analysts and scientists) consume that data.