Snowplow is a real-time event data collection platform, helping data teams collect rich user data across web and mobile apps. Dataform makes it easy to model this data into clean, reliable, tested datasets to enable insight and power your company’s analytics.
The raw data tables Snowplow creates are a great starting point for analytics. Transforming the datasets into well structured, de-normalised tables - for example sessions or page_views - speeds up future analysis. With Dataform you can schedule and keep up to date the core Snowplow web model tables.
The Dataform web IDE is natively integrated with GitHub and GitLab. Version controlling your SQL has never been easier: create branches, commit changes, revert files and create pull requests without ever needing to touch the command line.
Dataform’s built in SQLX functions enable us to infer dependencies and automatically build the dependency graph for your transformation pipeline.
Being able to produce analytics tables that we are confident in the output of (because of assertions) and are as up to date as we need them to be (because of scheduling) makes our lives really easy. The UI is incredibly easy and intuitive to use, meaning we spend little of our time setting these things up, and most of our time writing SQL!
I love the dependency tree in Dataform. For me this is a central place for sanity checking my data flows, understanding if I'm reimplementing a dataset which already exists, and verifying logic. Secondly, I love SQLX for generating SQL of a similar structure again and again, it really speeds up development and let's your abstract away logic.
Having modeled data using other tools in the past, this is much simpler and an easier environment to code in. The code compiles in real time and lets you know if there are errors in the syntax. It also helps generate a dependency graph for the data pipeline which is insanely useful.