Data Orchestration

Data Orchestration¶

"Most data outages are just bad communication bugs."

Scheduling, coordinating, and managing data pipelines.

Overview¶

graph TD
    A[Ingest] --> B{Validate}
    B -->|Pass| C[Transform]
    B -->|Fail| Z[Quarantine]
    C --> D{Publish}
    D -->|Success| E[Monitor]
    D -->|Retry| C
    E --> F[Alert on Issues]

    G[Schedule] -.Triggers.-> A
    H[Retry Logic] -.Handles.-> D
    I[Dependencies] -.Manages.-> A

    style A fill:#80deea
    style B fill:#fff9c4
    style C fill:#80deea
    style D fill:#fff9c4
    style E fill:#b2dfdb
    style Z fill:#ffccbc

Workflow orchestration managing dependencies and retries.

Data orchestration is about coordinating multiple data pipelines, managing dependencies, handling failures, and ensuring data flows reliably through your platform.

Key Topics¶

Airflow ¶

Apache Airflow for workflow orchestration.

Learn about: - Airflow concepts (DAGs, tasks, operators) - DAG design patterns - Best practices - Monitoring and troubleshooting

dbt ¶

Data Build Tool for SQL-based transformations.

Learn about: - dbt concepts (models, tests, macros) - Project structure - Testing framework - Documentation generation

Orchestration Tools¶

Apache Airflow¶

Best for: Complex workflows, Python-based

Pros: - ✅ Mature, widely adopted - ✅ Rich ecosystem - ✅ Flexible (Python-based) - ✅ Good UI and monitoring

Cons: - ❌ Requires operations - ❌ Can be complex - ❌ Resource-intensive

dbt¶

Best for: SQL-based transformations, analytics engineering

Pros: - ✅ SQL-based (accessible) - ✅ Great testing framework - ✅ Documentation generation - ✅ Version control friendly

Cons: - ❌ SQL-only - ❌ Requires orchestration (Airflow, etc.)

Prefect / Dagster¶

Best for: Modern Python orchestration

Pros: - ✅ Better developer experience - ✅ Modern architecture - ✅ Good testing support

Cons: - ❌ Less mature than Airflow - ❌ Smaller ecosystem

Data Processing - What to orchestrate
Data Quality - Quality checks in pipelines

Next: Airflow →

Data Orchestration