declarative-warehouse-automation

What You'll Build

A complete DataOps platform that treats your data warehouse like software - with version control, automated testing, and safe deployments. Data teams can push changes to production multiple times per day without breaking dashboards.

Why This Project Matters

• Solve Real Pain: 67% of data teams deploy manually, causing outages and slow delivery
• Enterprise Skills: Learn practices used at top tech companies for data reliability
• Career Growth: DataOps engineers command 20-30% salary premiums

Tech Stack Explained for Beginners

Technology	What it is	Why you'll use it
Snowflake	Cloud data warehouse platform	Your target database - where business data lives
dbt	Data transformation tool using SQL	Define data models as code with built-in testing
SQLMesh	Next-gen transformation framework	Provides "time travel" between data versions
Airflow	Workflow orchestration platform	Schedules and monitors your data pipelines
Terraform	Infrastructure-as-code tool	Manages Snowflake resources programmatically
Monte Carlo	Data observability platform	Detects data quality issues automatically

Step-by-Step Build Plan

1. Week 1: Set up Snowflake trial and development environment
2. Week 2: Create dbt project with sample e-commerce models
3. Week 3: Implement SQLMesh for environment management
4. Week 4: Build CI/CD pipeline with automated testing
5. Week 5: Add blue-green deployment strategy
6. Week 6: Implement monitoring and rollback procedures
7. Week 7: Create disaster recovery runbooks

Detailed Requirements

Functional Requirements

• Development Workflow:
  • Isolated dev environments per developer
  • Feature branch deployments for testing
  • Automated PR checks (schema, performance)
  • One-click promotion to production
• Data Models:
  • 20+ dbt models (staging, intermediate, marts)
  • Full test coverage (unique, not null, relationships)
  • Documentation with business definitions
  • Incremental processing for large tables
• Deployment Features:
  • Blue-green deployments (zero downtime)
  • Automatic rollback on test failure
  • Deployment history and audit trail
  • Break-glass emergency procedures

Technical Requirements

• Performance:
  • Model builds complete in < 10 minutes
  • Blue-green switch in < 30 seconds
  • No query disruption during deploy
  • Warehouse auto-suspend at $10/day
• Quality Gates:
  • 100% of models have primary key tests
  • Data freshness SLA monitoring
  • Row count variance checks (±5%)
  • Schema change detection
• Operations:
  • Full deployment rollback in < 1 minute
  • Automated backups before changes
  • Slack notifications for deployments
  • Cost tracking per model/pipeline

Prerequisites & Learning Path

• Required: SQL proficiency and basic Git knowledge
• Helpful: Experience with any data warehouse
• You'll Learn: DataOps, warehouse optimization, enterprise deployment patterns

Success Metrics

• Deploy 50+ changes without downtime
• Achieve <1% failed deployment rate
• Reduce deployment time from hours to minutes
• Maintain data freshness SLA of 99.9%
• Keep Snowflake costs under $300/month