Our team had the pleasure of speaking to seven leading data practitioners about their data migration experiences—the good, the bad, and the ugly. Learn about their strategical and technical best practices for faster, less stressful data migrations 👇 get.datafold.com/x-dms-ryan

Every data migration has a hero…or a villain 👀 🏆 We talked to practitioners across the industry to learn from real-world successes (& disasters) in The Data Migration Universe—best practices, hard lessons & a readiness check. get.datafold.com/x-dms

🦸‍♂️ Not all data engineers wear cape, but they should. Migrations are high-stakes, high-risk, & full of villains (👋 misaligned timelines). Enter The Data Migration Universe for real stories, best practices & a readiness check. Are you migration-ready? get.datafold.com/x-dms

Data-driven design… in cookie form! 🖤🍪 Our team's holiday baking activity was the perfect mix of innovation and fun with the Datafold logo as a 3D-printed cookie cutter!

We’ve heard the challenges data teams face—ensuring data integrity amidst scaling pipelines & growing complexity. Today, we’re taking a big step to address those pains. Introducing Monitors in Datafold: Data monitoring that starts upstream. get.datafold.com/monitors-bl…

Ensuring high data quality during data deployment is crucial as it sets the foundation for downstream applications. There are 4 common ways that practitioners have approached data quality testing during this stage. Learn more on how they rank: datafold.com/data-quality-gu…

Getting data testing right is a moving target. Codebases grow in complexity over time, code diffs just don’t show the impact, and there are too many “unknown unknowns” that can’t be accounted for in custom tests.

☎ Calling all friends on GCP: Datafold is now available on Google Cloud Marketplace! datafold.com/blog/datafold-i…

With workflows that make sense, checklists against errors, and an automated data quality system orchestrating everything, sit back and watch your developer velocity increase without sacrificing data quality:

A proactive data quality strategy is governed by 3 key principles: 1. Shifting data testing to the left: Test early and often during the development cycle.

#dbt v1.8 is out with new unit testing for dbt projects! Learn from @glebmm about its benefits over traditional assertion tests, when to use it, & implementation tips. datafold.com/blog/dbt-unit-t…

How does Datafold's Data Diffing differ from your standard dbt tests?