Course Overview
About Course
Great Expectations is an open-source Python framework that empowers data teams to define, document, and enforce data quality through declarative “Expectations.” Participants learn how to install GX, initialize Data Contexts, and connect to data sources like pandas, Spark, SQL, and cloud systems
Central to GX are Expectation Suites—collections of tests such as schema validation, missing or unique values, and conditional logic. Suites are applied via validations and visualized through Data Docs—auto-generated HTML reports delivering clear, actionable insights
Training covers Checkpoints for scheduled validations and integration with ETL tools, version control, CI/CD pipelines, and team collaboration using GX Cloud. Advanced modules explore custom Expectation creation, profiling auto-generation for quicker onboarding, and validating data integrity across complex relationships .
By the end of the 40-hour course, learners will be able to architect production-grade data quality frameworks—automated, scalable, and accessible—ensuring both technical teams and business stakeholders trust the data powering decisions.
Course Syllabus
- Introduction & Setup (4 h)
- What is Great Expectations? Overview & ecosystem.
- Installation, CLI walkthrough.
- Creating your first Data Context, Data Source, and Batch.
- Understanding Expectations (6 h)
- What is an Expectation? Core concepts.
- Basic expectations: schema, column types, uniqueness, missingness
- Conditional & cross-column expectations.
- Expectation Suites & Validation (6 h)
- Building and editing Suites.
- Executing validations via CLI and API.
- Interpreting validation results and Data Docs 4. Checkpoints & Automation (6 h)
- Creating and configuring Checkpoints.
- Scheduling validation runs.
- Integrating GX into ETL pipelines with Prefect, Airflow, GitHub Actions
- Data Integrity & Use Cases (6 h)
- Referential & hierarchical integrity checks
- Volume, freshness, cardinality, and semantic-type expectations
- Integrations & Scaling (6 h)
- Connectors: pandas, Spark, SQL, cloud storage.
- Version control, collaboration.
- Great Expectations Cloud overview, multi-team workflows .
- Profiling & Documentation (4 h)
- Auto-profiling data to generate Expectation Suites.
- Building Data Docs for visibility and sharing
- Advanced Topics (6 h)
- Custom Expectation development.
- Integrating with Feature Stores (e.g., Hopsworks).
- Performance tuning & enterprise strategies
Key Features
- Hands-on labs using real-world datasets.
- Collaborative exercises with CLI and API workflows.
- Automated profiling to bootstrap expectations.
- CI/CD integration demos with GitHub Actions, Airflow, Prefect.
- Live Data Docs sessions to engage technical and business stakeholders.
- Enterprise integration strategies including cloud-based deployment and scalable pipelines.



