GX: Syllabus

Mastering Great Expectations: Data Validation and Unit Testing with Python

Great Expectations (GX) is an open-source Python framework for data validation, profiling, and testing. This book provides a hands-on approach to mastering Great Expectations, focusing on real-world applications, integrations, and best practices for maintaining data quality at scale.

Module 1: Introduction to Data Validation and Great Expectations#

• Understanding the importance of data validation in modern workflows
• Overview of Great Expectations: Key features and capabilities
• Installing and setting up Great Expectations in a Python environment
• Creating and managing Great Expectations projects

Module 2: Expectations and Data Quality Rules#

• Understanding Expectations and how they work
• Defining Expectations for numerical, categorical, and textual data
• Implementing custom Expectations for business-specific rules
• Best practices for writing reusable and scalable Expectations

Module 3: Connecting Great Expectations to Data Sources#

• Connecting to local CSV, JSON, and Parquet files
• Integrating Great Expectations with PostgreSQL and other relational databases
• Using Spark and Pandas data sources for validation
• Handling large datasets efficiently with batch processing

Module 4: Automated Data Profiling and Validation#

• Generating automatic data documentation with Data Docs
• Running validation checks on new datasets
• Setting up data validation workflows for ETL pipelines
• Capturing and handling validation failures

Module 5: Unit Testing and CI/CD Integration#

• Writing unit tests for data validation
• Using pytest with Great Expectations for automated testing
• Integrating Great Expectations into CI/CD pipelines
• Implementing data quality gates in production environments

Module 6: Data Monitoring and Anomaly Detection#

• Implementing real-time data validation for streaming pipelines
• Detecting anomalies and outliers with Expectations
• Using Data Assistants for automated profiling
• Alerting and logging validation errors with monitoring tools

Module 7: Advanced Great Expectations Configurations#

• Configuring validation actions and result stores
• Working with Expectation Suites and Data Context
• Using Checkpoints for scheduled data validation
• Managing and versioning Expectations in collaborative environments

Module 8: Deploying Great Expectations in Production#

• Deploying Great Expectations on AWS, GCP, and Azure
• Running Great Expectations with Apache Airflow
• Best practices for scaling data validation workflows
• Securing sensitive data in validation pipelines

Hands-On Projects

Project 1: Building a Data Quality Dashboard with PostgreSQL#

• Set up Great Expectations for PostgreSQL data validation
• Define and execute Expectations for a structured dataset
• Generate and visualize validation reports

Project 2: Automating ETL Validation with Great Expectations#

• Integrate data validation into an ETL pipeline
• Run validations before and after data transformations
• Automate validation workflows with Airflow and CI/CD

Project 3: Implementing Data Quality Monitoring for Real-Time Pipelines#

• Set up Great Expectations with a streaming data source
• Implement real-time anomaly detection and alerting
• Store validation results for long-term analysis

Project 4: Creating a Reusable Data Validation Framework#

• Build a modular, reusable validation suite for multiple datasets
• Configure Checkpoints for scheduled and on-demand validation
• Automate report generation for data stakeholders

Project 5: Deploying a Scalable Data Quality System in Production#

• Deploy Great Expectations in a cloud environment
• Secure and optimize validation workflows for large datasets
• Monitor validation performance and optimize resource usage

References#

• Great Expectations Official Documentation
• PostgreSQL Documentation
• Pytest for Data Testing
• Data Validation Best Practices