This course establishes a technical framework for maintaining reliable data systems in production environments. We focus on the intersection of Data Engineering and reliability, often referred to as Data Reliability Engineering. You will learn to implement automated quality checks, configure observability for pipelines, and manage data governance through code.
The curriculum moves beyond theory into the practical application of testing frameworks, anomaly detection algorithms, and metadata standards. We examine how to maintain trust in data assets by preventing schema drift, detecting freshness issues, and enforcing policies within the deployment pipeline. This content targets engineers who need to operationalize data quality and ensure their architectures withstand the demands of scaling organizations.
Prerequisites Python & data pipeline concepts
Level:
Data Quality Testing
Write and deploy automated assertions to validate data accuracy, completeness, and consistency.
Observability Implementation
Build monitoring systems to track freshness, volume, and schema changes in real-time.
Programmatic Governance
Implement policy-as-code and role-based access controls to manage data security and compliance.
Data Lineage
Trace data dependencies and impact analysis using industry standards like OpenLineage.
© 2026 ApX Machine LearningEngineered with