Great Expectations

Founded by James Campbell in 2017

Validate, test, and document pipeline data to prevent bad records downstream

Pricing model

Free

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Great Expectations to test, validate, and document data across your pipelines. It lets you write automated assertions, called Expectations, directly in Python to define how your tables and columns should look. Whenever your pipelines execute, it validates your data against these rules and flags errors before issues reach downstream users or databases. The system automatically creates visual reports called Data Docs so engineers and business stakeholders can review data health in clear language without digging into raw code.

What Great Expectations does

Define test assertions for column types and valuesRun automated data validation suites inside pipeline runsPublish rendered HTML Data Docs for inspectionSend failure notifications to engineering teamsBlock faulty data batches from entering production warehousesInspect profiling results on newly connected data sourcesWrite declarative data tests in Python or JSONGenerate automatic HTML documentation called Data DocsConnect directly to data sources and pipeline orchestratorsTrigger automated alert notifications when checks failPrevent invalid records from moving into downstream systemsValidate schemas, null counts, and column distributions

Pricing breakdown

Pricing model: Free

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