Apache Airflow as the data pipeline orchestrator
Apache Airflow is the dominant open-source data pipeline orchestrator in enterprise data engineering. For clients running scheduled data pipelines — ingestion from source systems, transformation jobs, ML training workflows, reporting data prep — Airflow is typically the orchestrator the data team already runs or is moving toward. Its Python-based DAG definition model, rich operator ecosystem, and mature UI for pipeline observability have made it the default for most enterprise data orchestration.
How Thoughtwave integrates Airflow
Our Airflow engagements cover:
- DAG design following enterprise patterns — idempotent tasks, explicit retry logic, pool-based resource management, proper use of XComs for small-state handoff.
- Managed Airflow deployments — Amazon MWAA, Google Cloud Composer, Astronomer, or self-hosted Airflow depending on client preference and scale.
- dbt + Airflow integration for analytics-engineering pipelines where dbt handles transformation and Airflow handles orchestration.
- AI workload orchestration — scheduled embedding updates, RAG index refreshes, model-retraining pipelines, and evaluation workflows orchestrated through Airflow alongside traditional data pipelines.
- Migration from legacy schedulers (Informatica, cron-scripts, homegrown schedulers) to Airflow with observability and reliability upgrades along the way.
- Data-quality checks integrated into pipelines using Great Expectations or dbt tests with Airflow orchestrating the validation cadence.
For clients where Airflow is the existing orchestrator, our engagements extend it rather than replacing it — particularly for AI-workload orchestration where the client's team is more comfortable with Airflow's Python model than with newer alternatives.
Authentication and deployment
Airflow deployments run under the client's identity provider via Airflow's authentication plugin ecosystem (OAuth, LDAP, Kerberos). Managed Airflow offerings integrate with the cloud provider's IAM model. Secrets management uses the client's existing secrets infrastructure (Vault, AWS Secrets Manager, Azure Key Vault).
When Airflow still wins
Airflow remains the right choice for enterprise data orchestration in 2026 despite newer alternatives (Prefect, Dagster, Temporal) gaining traction. The operator ecosystem, the team familiarity, and the broad community support make it the default. Our engagements recommend alternatives only when a specific requirement (event-driven rather than schedule-driven workloads, much simpler operational model, specific compliance requirement) argues for a different tool.