Data Quality with Great Expectations
Great Expectations is an open-source Python framework that automates data quality checks by validating datasets against codified rules called “expectations.”
When to Use
Use Great Expectations (GE) in ETL pipelines, data warehouses, or ML training pipelines where schema validation, null checks, and data consistency matter. It ensures that data is trustworthy before it powers analytics or models.
Example
Imagine a sales ETL job: GE can assert that order_id is always unique and order_amount > 0. If data fails validation, it triggers alerts so issues are caught early.
Want to prepare for interviews and system design deeply?
Explore Grokking System Design Fundamentals, Grokking the Coding Interview, or practice with Mock Interviews with ex-FAANG engineers.
Why Is It Important
Poor data quality leads to bad decisions. GE enforces data contracts, making analytics reliable and machine learning results robust. It saves teams from downstream failures.
Interview Tips
When asked about GE, highlight how it integrates into CI/CD pipelines, generates “Data Docs” for collaboration, and codifies business logic into reproducible rules. Show you understand both the practical workflow and the governance value.
Trade-offs
Pros: early error detection, team-wide trust, and automated documentation. Cons: extra setup, ongoing maintenance, and potential false positives if expectations are too strict.
Pitfalls
Common mistakes include over-testing trivial checks or failing to update expectations as data evolves. Relying only on GE without broader monitoring can leave blind spots.
GET YOUR FREE
Coding Questions Catalog
$197
$78
$78
