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Dagster Orchestration

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Dagster is a data orchestration platform for building, testing, and monitoring data pipelines. Unlike Airflow (task-oriented), Dagster models pipelines as assets (data, ML models, reports) with dependencies. Engineers define compute units (ops), declare data dependencies, and Dagster orchestrates execution, handles failures, and traces lineage. Senior Dagster architects earn 10-15% premium because they ship pipelines that are 50% shorter, more maintainable, and observable than Airflow. Learning: 6-8 weeks.

Vad är Dagster Orchestration

Dagster is a modern data orchestration platform for building, testing, and monitoring data pipelines. Unlike Airflow (which treats pipelines as DAGs of tasks), Dagster models pipelines as asset dependencies, named data outputs with clear lineage. Example: Extract data from API → Transform with Pandas → Load to warehouse → Compute metrics → Update dashboard. Dagster tracks each asset, knows which upstream assets changed, and reruns downstream jobs as needed.

🔧 VERKTYG & EKOSYSTEM
Dagster CoreDagster CloudDagit (web UI)Sensor operatorsResource abstractionsAsset definitionsPython decoratorsPandas/PolarsCloud storage (S3, GCS)Data warehouses (Snowflake, BigQuery)

📋 Innan du börjar

Python

💰 Lön per region

OmrådeNybörjareMidErfaren
USA$85k$140k$215k
UK£52k£85k£130k
EU€58k€92k€140k
CANADAC$80kC$135kC$205k

❓ Vanliga frågor

When should I use Dagster instead of Airflow?
Dagster is better for asset-heavy pipelines (many data outputs). Airflow is better for task-heavy workflows (many independent jobs). Dagster has better error handling and observability. Choose Dagster if you care about lineage and data quality.
What's the difference between ops and assets?
Ops are compute units (do something). Assets are named data outputs (op produces data → asset). Assets are better because they're reusable, versionable, and have lineage. Modern Dagster prefers assets over ops.
How do I test Dagster pipelines?
Dagster provides in-process execution for unit testing. Run ops/assets without external resources. Mock databases, APIs. Test is fast, deterministic, and doesn't touch production.
Can Dagster run on Kubernetes?
Yes. Dagster Cloud runs on K8s. Each job spawns a pod, executes, then terminates. Great for handling variable load. Requires K8s cluster (EKS, GKE, etc.).
How do I handle dynamic parallelization?
Use `DynamicOutput` to spawn parallel jobs at runtime based on data (e.g., process each partition in parallel). Dagster handles fan-out/fan-in automatically.

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