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Airflow Automation
We build Apache Airflow solutions that schedule, monitor, and manage complex workflows. From data pipelines to business processes—with full visibility and reliability.
Automation & Workflow
Modern orchestration tools and infrastructure
Production-ready Airflow implementation with documentation and training.
Well-structured DAGs for your workflows with testing and documentation.
Airflow environment on AWS MWAA, Cloud Composer, or Kubernetes.
Alerting, logging, and dashboards for workflow visibility.
Operational guides and hands-on training for your team.
A proven approach to building reliable workflow automation.
Document existing processes, dependencies, schedules, and failure modes.
Build Airflow DAGs with proper error handling, retries, and idempotency.
Deploy Airflow on managed service or Kubernetes with monitoring.
Migrate existing jobs, train your team, and establish runbooks.
Flexible options for Airflow adoption and management.
Migrate 3-5 workflows to Airflow with managed deployment.
$10,000 - $20,000
Complete Airflow environment with multiple DAGs and team training.
$25,000 - $45,000
Ongoing DAG development, maintenance, and support.
$5,000 - $12,000/mo
Results from Airflow implementations we've delivered.
"We replaced 50+ cron jobs with Airflow. Finally have visibility into what's running and what failed."
"Workflow retries are automatic now. We used to spend hours manually rerunning failed jobs."
"The dependency graph visualization alone was worth the migration. We can actually understand our pipelines."
"We replaced 50+ cron jobs with Airflow. Finally have visibility into what's running and what failed."
Managed services (MWAA, Cloud Composer) reduce ops burden but cost more. Self-hosted on Kubernetes gives flexibility. We recommend based on your team and budget.
DAGs include configurable retries, exponential backoff, and alerting. Failed tasks can be re-run independently without reprocessing the entire workflow.
Yes. Airflow has operators for most databases, cloud services, and APIs. Custom operators can connect to anything with an API or SDK.
Python developers can write DAGs within a week. We provide training and templates to accelerate adoption.
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