Data Quality Tools for Every Data Team

We data people need better tools, and a better quality of life. But as mighty as dbt Labs is, we can't expect them to solve all of our needs. I've been getting a kick out of building data tools at every company I worked at, most notably at Lyft. And Alex and I started Datafold to enable every data and analytics engineer with the tooling that would make their workflow more productive and enjoyable. We're doing this by automating the most tedious and error-prone tasks in the Modern Data Stack (cloud DWH + dbt) such as testing changes to ELT code and tracing data lineage.

Over the past two years, we got to work with and learn from some of the best Data teams out there, but as much as we'd want to stick to large-check high-touch customer engagements, we wouldn't succeed in our mission of enabling as many teams with the essential tools as possible continuing with a top-down custom-priced enterprise motion.

That's why I am excited to share that Datafold now has a Cloud plan starting at $799 / month, including turnkey column-level lineage, fully automated dbt model testing in CI, and ML-powered SQL alerts. There is also 1-month free trial and a free plan for smaller teams. Folks with large Security and Procurement teams – we got you covered as well.

Improve your quality of life with Datafold

How Shopify & Spotify DAGify their data workflows

Speaking of quality of life, few things affect the data engineering workflow as profoundly as the choice of the orchestrator. (assuming everyone figured to move to a cloud warehouse by now). What's the perfect orchestrator today? Airflow is used by 250K teams but, let's be honest, is brutally user-unfriendly by modern standards. Among the most data-driven companies, there is no consensus: Shopify doubles down and scales Airflow to 10K+ DAGs, Spotify switched from homegrown Luigi to Flyte built by Lyft. Lyft runs both Flyte and Airflow at scale and thus has a unique perspective on both. And Mapbox is quietly and incrementally migrating to Dagster.

Data Stories: Cities with Nice Weather

"I live near Toronto. It’s springtime, and currently about 30 °C. In my opinion, Toronto is too hot in the summer and too cold in the winter. I’d like to know which cities have the least deviation from a tolerable average temperature..." – beautiful data narratives in R-Markdown are their own genre of content. Sort of like classic rock.

Before You Go

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