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Data Transformation

Master plan for building ETL/ELT pipelines and data transformation workflows. Covers pipeline orchestration, data quality frameworks, incremental processing strategies, and modern analytics engineering practices.

Key Topics​

  • ETL vs. ELT Patterns: When to transform before or after loading, trade-offs and architecture
  • Pipeline Orchestration: DAG design, dependency management, scheduling, and retries
  • Incremental Processing: Change detection, watermark strategies, and upsert patterns
  • Data Quality Frameworks: Validation rules, anomaly detection, and data profiling
  • Idempotency Patterns: Ensuring safe pipeline re-runs and exactly-once processing
  • Error Handling: Dead letter queues, poison pill detection, and graceful degradation
  • Batch vs. Micro-Batch: Processing window selection and latency requirements
  • Data Lineage Tracking: Capturing transformations, dependencies, and impact analysis
  • Testing Strategies: Unit tests for transformations, integration tests, data diff testing
  • Analytics Engineering: dbt workflows, model organization, and documentation practices

Primary Tools & Technologies​

Orchestration Platforms:

  • Apache Airflow (Python-based DAG orchestration)
  • Prefect (modern Python workflow engine)
  • Dagster (data-aware orchestration)
  • Temporal (durable execution engine)
  • Azure Data Factory, AWS Glue, Google Cloud Composer (managed services)

Transformation Frameworks:

  • dbt (SQL-based analytics engineering)
  • Apache Spark (distributed batch/stream processing)
  • Pandas, Polars (Python data manipulation)
  • SQL stored procedures and views

Data Quality:

  • Great Expectations (Python data validation)
  • dbt tests (SQL-based quality checks)
  • Soda Core (data quality checks as code)
  • Monte Carlo, Datafold (automated data observability)

Data Integration:

  • Airbyte (open-source ELT connector platform)
  • Fivetran (managed ELT service)
  • Singer taps (lightweight connectors)
  • Apache NiFi (visual dataflow programming)

Version Control & CI/CD:

  • Git for pipeline code
  • dbt Cloud, Astronomer for CI/CD
  • GitHub Actions, GitLab CI for automation

Integration Points​

Upstream Dependencies:

  • Data Architecture: Schema design informing transformation logic
  • Streaming Data: Real-time transformations and CDC integration
  • SQL Optimization: Query tuning for transformation performance
  • Secret Management: Secure handling of database and API credentials

Downstream Consumers:

  • Data Visualization: Clean, modeled data ready for BI tools
  • Analytics: Aggregated datasets for reporting and dashboards
  • Machine Learning: Feature engineering and training dataset preparation

Cross-Functional:

  • Performance Engineering: Pipeline optimization and resource tuning
  • Monitoring & Alerting: Data pipeline health, SLA tracking, and failure alerts
  • Documentation: Automated lineage diagrams and data dictionaries

Status​

Master Plan Available - Comprehensive guidance for data transformation pipelines, covering Airflow, dbt, Spark, and modern analytics engineering workflows.

Part of the Data Engineering skill collection focused on building reliable, maintainable data transformation pipelines.