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# ETL

The ETL (Extract, Transform, Load) layer handles all data movement and transformation within the platform. It is designed to be governed, reusable, and observable.

# Pipeline Architecture

# Overview

ETL pipelines move data through the medallion architecture:

Sources → Bronze → Silver → Gold → Consumption

Each transition applies specific transformations and quality gates.

# Design Principles

  1. Idempotency: Running a pipeline multiple times produces the same result
  2. Incremental processing: Process only new or changed data when possible
  3. Lineage capture: Track data origins and transformations
  4. Testability: Every pipeline includes automated tests

# Core Components

# Orchestration

The orchestration layer manages pipeline scheduling and dependencies:

Capabilities:

  • Schedule-based execution (cron-like)
  • Event-triggered execution
  • Dependency management between pipelines
  • Retry logic and failure handling
  • Backfill support for historical data

Best Practices:

  • Define clear ownership for each pipeline
  • Set appropriate timeouts and retries
  • Monitor execution duration trends
  • Document dependencies explicitly

# Workers

Compute resources that execute transformations:

Considerations:

  • Right-size compute for the workload
  • Use auto-scaling where appropriate
  • Monitor resource utilization
  • Separate dev/prod compute pools

# Blueprints

Reusable patterns for common transformation scenarios:

Purpose:

  • Accelerate new pipeline development
  • Enforce consistent patterns
  • Reduce errors through proven templates
  • Enable self-service for common cases

Blueprint Examples:

Blueprint Description Use Case
CDC Ingestion Load incremental changes from databases Transactional data
API Loader Extract data from REST APIs External integrations
Log Parser Structure and partition log data Application logs
Aggregation Create summary tables Reporting metrics
Snapshot Point-in-time data capture Slowly changing dimensions

# Data Testing

Automated validation ensures data quality:

Test Types:

  1. Schema tests: Column presence, types, nullability
  2. Uniqueness tests: Primary key violations
  3. Referential tests: Foreign key relationships
  4. Range tests: Values within expected bounds
  5. Freshness tests: Data recency checks
  6. Custom assertions: Business-specific rules

Implementation:

  • Tests run as part of pipeline execution
  • Failures block downstream processing
  • Results logged for audit and debugging
  • Alerts sent on test failures

# Alerting and Playbooks

Operational monitoring and incident response:

Alerting:

  • Pipeline failures
  • Data quality test failures
  • Unusual execution times
  • Cost threshold breaches
  • Data freshness violations

Playbooks:

  • Documented response procedures for common issues
  • Escalation paths
  • Runbook automation where possible

# Tenancy Controls

Multi-tenant data isolation within ETL:

Considerations:

  • Tenant-specific pipelines vs. shared pipelines
  • Data partitioning by tenant
  • Compute isolation if required
  • Cost attribution per tenant

# Schema Enforcement

# Schema Validation

Validate incoming data against expected schemas:

  • Schema registry: Central repository of schema definitions
  • Validation on ingestion: Reject or quarantine non-conforming data
  • Schema evolution: Handle backward-compatible changes gracefully

# Schema Evolution

Handle changes to source system schemas:

  1. Additive changes: New columns added with defaults
  2. Breaking changes: Require coordinated migration
  3. Documentation: Update catalog on schema changes

# Lineage Metadata

# Capturing Lineage

Track data flow through the platform:

Metadata captured:

  • Source tables and columns
  • Transformation logic applied
  • Output tables and columns
  • Execution timestamps
  • Job identifiers

# Benefits

  1. Impact analysis: Understand downstream effects of changes
  2. Root cause analysis: Trace data issues to source
  3. Compliance: Document data flows for auditors
  4. Trust: Users can verify data origins

# Pipeline Lifecycle

# Development

  1. Design: Document requirements and approach
  2. Build: Implement using blueprints where applicable
  3. Test: Unit tests, integration tests, data tests
  4. Review: Code review and documentation

# Deployment

  1. Staging: Test in non-production environment
  2. Approval: Required sign-off for production
  3. Deploy: Automated deployment process
  4. Verify: Post-deployment validation

# Operations

  1. Monitor: Execution, performance, quality
  2. Maintain: Address issues and improvements
  3. Optimize: Cost and performance tuning
  4. Retire: Sunset unused pipelines

# Best Practices

# Do

  • Start with existing blueprints
  • Include data tests in every pipeline
  • Document business logic in code
  • Monitor pipeline costs
  • Set up alerting from day one

# Don't

  • Create one-off, undocumented pipelines
  • Skip testing to save time
  • Ignore cost implications
  • Hardcode values that may change
  • Create deep chains of views

# Anti-Patterns to Avoid

  1. View inception: Views referencing views referencing views (costly, hard to debug)
  2. **SELECT ***: Always specify columns explicitly
  3. Orphaned pipelines: Pipelines that run but output isn't used
  4. Missing tests: Pipelines without quality gates
  5. Manual interventions: Processes requiring regular human action

# Related Sections