Module 6: CI/CD for Data

Overview

This module covers CI/CD practices for data platforms, including data CI/CD pipelines, data diffing strategies, and deployment patterns for data models and pipelines. Unlike software CI/CD, data CI/CD must handle data quality, schema changes, and data validation.

Module Contents

Document	Description	Key Topics
Data CI/CD Pipelines	CI/CD for data	Testing, validation, automation
Data Diffing	Data comparison	Tools, strategies, automation
Deployment Strategies	Data deployments	Blue-green, canary, rollback

Data CI/CD Pipeline

Data Diffing Strategies

Strategy	Use Case	Tool
Row-by-row	Exact match	datafold, daff
Aggregate comparison	Statistical	Great Expectations
Schema diff	Schema changes	dbt, Soda
Sample comparison	Quick validation	Custom SQL

Deployment Strategies

Blue-Green Deployment

Canary Deployment

Testing Pyramid for Data

Key Concepts

Data vs. Software CI/CD

Dimension	Software CI/CD	Data CI/CD
Artifacts	Binaries, containers	Models, transformations, data
Testing	Unit, integration	Data tests, quality checks
Deployment	Rolling update	Blue-green, canary
Rollback	Revert code	Restore data, revert schema
Validation	Functional tests	Data validation, statistics

Data Quality Gates

# Example quality gates
quality_gates:
  - name: row_count_check
    condition: row_count > 0
    severity: critical

  - name: null_check
    condition: null_ratio < 0.05
    severity: warning

  - name: schema_drift
    condition: schema_match
    severity: critical

  - name: distribution_check
    condition: ks_test > 0.95
    severity: warning

Learning Objectives

After completing this module, you will:

Design data CI/CD pipelines: Automated testing and deployment for data
Implement data diffing: Compare data across environments and time
Choose deployment strategies: Blue-green vs. canary for data
Set up quality gates: Automated data validation
Handle rollback: Rollback strategies for data deployments

Module Dependencies

Next Steps

Estimated Time to Complete Module 6: 4-6 hours