Orchestration

Apache Airflow

The industry-standard orchestrator for data workflows

https://airflow.apache.org

What It Is

Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring workflows. Originally developed at Airbnb, Airflow has become the most widely adopted orchestration tool in data engineering. Its Python-based DAG definitions, rich operator ecosystem, and extensible architecture make it the go-to choice for teams that need reliable, observable, and maintainable data pipeline orchestration.

How We Use It

Airflow is our primary orchestration platform. We use it to coordinate everything from simple ETL schedules to complex multi-system data platform operations. Our deployments run dbt transformations, trigger Spark jobs, manage Airbyte syncs, coordinate ClickHouse data loading, and handle cross-system dependencies — all with proper alerting, retry logic, and SLA monitoring. We deploy Airflow on Kubernetes (via Helm), use managed services (MWAA, Cloud Composer, Astronomer), and maintain custom deployments.

Our Expertise

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DAG Development

We build clean, maintainable Airflow DAGs: TaskFlow API, dynamic task generation, XComs, branching, and trigger rules.
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Deployment & Infrastructure

We deploy Airflow on Kubernetes (Helm), configure managed services (MWAA, Cloud Composer), and manage custom installations.
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Monitoring & Alerting

We set up SLA monitoring, failure callbacks, Slack/PagerDuty alerts, and custom health checks.
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Custom Operators

We develop custom operators and hooks for proprietary systems and specialized integrations.
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Performance & Scaling

We tune Airflow for performance: executor selection (Celery, Kubernetes), connection pooling, and DAG parsing optimization.