Data Quality & Testing Fundamentals
Learn data quality principles, testing strategies, and observability practices essential for building reliable data pipelines.
Level:
Intermediate
Tools:
Great Expectations
Soda Core
dbt tests
elementary
Monte Carlo
Skills You'll Learn:
Data quality assessment
Testing strategies
Data observability
Schema validation
Data contracts
Step 1: Understanding Data Quality
- 1Understand the 6 dimensions of data quality: completeness, accuracy, consistency, timeliness, validity, and uniqueness
- 2Learn about data quality metrics and KPIs used to measure and track quality over time
- 3Identify common data quality issues in real-world pipelines such as missing values, duplicates, and schema drift
- 4Understand the cost of poor data quality and its impact on downstream analytics and decision-making
Step 2: Data Testing Fundamentals
- 1Learn the difference between schema tests and data tests and when to apply each
- 2Understand freshness tests and volume tests to detect stale or missing data
- 3Build a testing pyramid for data pipelines covering unit, integration, and end-to-end tests
- 4Write assertions for data expectations including range checks, null checks, and referential integrity
Step 3: Great Expectations
- 1Install and configure Great Expectations in a Python project
- 2Create your first expectation suite defining rules for your datasets
- 3Run validations against datasets and interpret validation results
- 4Build data docs and configure checkpoints for automated validation runs
- 5Set up profiling for automatic expectation generation from sample data
Step 4: Soda Core & dbt Tests
- 1Install and configure Soda Core for data quality checks
- 2Write Soda checks using SodaCL to validate data quality rules
- 3Master dbt built-in tests including unique, not_null, relationships, and accepted_values
- 4Create custom dbt tests using both generic and singular test patterns
- 5Integrate testing into dbt workflows with test selection and failure severity levels
Step 5: Data Contracts
- 1Understand what data contracts are and why they matter for data mesh and decentralized architectures
- 2Define schema contracts between data producers and consumers using structured specifications
- 3Implement contract testing in your pipeline to catch breaking changes before deployment
- 4Handle schema evolution and breaking changes with versioning and migration strategies
- 5Learn about contract enforcement strategies including automated validation and governance policies
Step 6: Data Observability
- 1Understand the 5 pillars of data observability: freshness, volume, schema, distribution, and lineage
- 2Learn about anomaly detection patterns for identifying unexpected changes in data pipelines
- 3Set up monitoring and alerting for data quality using tools like elementary and Monte Carlo
- 4Understand Monte Carlo concepts and the principles of data reliability engineering
- 5Build a data quality dashboard to visualize quality metrics and trends across your data estate
Step 7: Data Quality in Production
- 1Implement circuit breaker patterns to halt pipelines when data quality falls below thresholds
- 2Design data quarantine strategies to isolate and remediate bad data without blocking pipelines
- 3Build data quality SLAs and define incident response procedures for quality failures
- 4Integrate data quality checks into CI/CD pipelines for continuous validation on every change
- 5Monitor and report on data quality trends over time to drive continuous improvement