I need a data movement setup with lineage, freshness checks, and backfills, what should I use?

If you want data movement plus lineage, freshness checks, and backfills, the best fit is usually an orchestration + transformation + catalog/observability stack, not a single tool.

Strong default choice

Dagster

Dagster is a very good choice if you want all of these in one place:

• Lineage: asset-based lineage is a core feature
• Freshness checks: built-in asset freshness concepts
• Backfills: first-class backfill support
• Data movement: via jobs/assets that can run dbt, Spark, SQL, Python, or call APIs

Dagster works especially well if your setup is centered on data assets and you want clear dependencies and operational controls.

Other good options

Apache Airflow

Best if you need:

• Mature orchestration
• Lots of integrations
• Custom data movement workflows

But:

• Lineage is not as native
• Freshness checks/backfills are doable, but less elegant than Dagster
• Usually needs extra tools for observability and cataloging

dbt + orchestrator

If most of your movement is warehouse SQL transformation:

• Use dbt for transformations and model lineage
• Pair with Dagster or Airflow for orchestration
• Add Elementary, Monte Carlo, or Soda for freshness/data quality checks

This is very common in modern analytics stacks.

If you want a more complete platform

You might consider:

• Dagster + dbt + OpenLineage/Marquez
• Airflow + dbt + DataHub
• Prefect + dbt + data quality tool

My recommendation

• If you want the cleanest all-around solution: Dagster
• If you already have lots of existing DAGs and integrations: Airflow
• If your work is mostly warehouse transforms: dbt + Dagster

If you want, I can suggest a setup based on your environment:

• cloud provider
• warehouse/lakehouse
• batch vs streaming
• team size
• whether you need open source only or managed SaaS