Skip to content
Deadline to Die

Data Quality & Validation

🛡️ Data Quality & Validation

In Data Engineering, “Bad data in = Bad decisions out.” We use automated quality gates to ensure our datasets are accurate, complete, and consistent.

🟢 Level 1: Foundations (The Expectations)

1. What is Great Expectations?

Great Expectations (GX) is a library for describing and validating your data.

2. The Expectation Suite

A collection of “Expectations” (assertions) like:

  • expect_column_values_to_not_be_null
  • expect_column_values_to_be_between
  • expect_column_values_to_match_regex

🟡 Level 2: Integration & Pipelines

3. Checkpoints

A Checkpoint combines a dataset, an Expectation Suite, and an action (like sending a Slack alert if the validation fails).

4. Data Docs

GX automatically generates interactive HTML reports from your validation results, allowing non-technical stakeholders to see the health of the data.

🔴 Level 3: Advanced Quality Gates

5. Silver-to-Gold Gating

Implement a “Quality Gate” in your pipeline. If the Silver table fails its Great Expectations check, the pipeline stops and never updates the Gold (Production) table.

6. Profiling

Use the GX Profiler to automatically generate a baseline set of expectations from an existing “Known Good” dataset.