Airflow Automation
Orchestrate any workflow with confidence.
We build Apache Airflow solutions that schedule, monitor, and manage complex workflows. From data pipelines to business processes—with full visibility and reliability.
- Custom DAG development for any workflow
- Managed Airflow deployment on AWS, GCP, or Kubernetes
- Migration from cron, Luigi, or custom schedulers
- Dependency management and parallel execution
- Alerting, retries, and failure handling
- Full observability with logging and metrics
Orchestrate any workflow with confidence.
Automation & Workflow
Technologies We Use
Modern orchestration tools and infrastructure
What we deliver
Production-ready Airflow implementation with documentation and training.
Airflow DAGs
Well-structured DAGs for your workflows with testing and documentation.
Managed deployment
Airflow environment on AWS MWAA, Cloud Composer, or Kubernetes.
Monitoring setup
Alerting, logging, and dashboards for workflow visibility.
Runbooks & training
Operational guides and hands-on training for your team.
How We Work
A proven approach to building reliable workflow automation.
Workflow Mapping
Document existing processes, dependencies, schedules, and failure modes.
DAG Development
Build Airflow DAGs with proper error handling, retries, and idempotency.
Infrastructure Setup
Deploy Airflow on managed service or Kubernetes with monitoring.
Migration & Training
Migrate existing jobs, train your team, and establish runbooks.
Engagement models
Flexible options for Airflow adoption and management.
Airflow pilot
Migrate 3-5 workflows to Airflow with managed deployment.
$10,000 - $20,000
Full platform build
Complete Airflow environment with multiple DAGs and team training.
$25,000 - $45,000
Managed Airflow
Ongoing DAG development, maintenance, and support.
$5,000 - $12,000/mo
Certifications & Partners
What clients are saying
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."
Frequently asked questions
Should we use managed Airflow or self-hosted?
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.
How do you handle job failures?
DAGs include configurable retries, exponential backoff, and alerting. Failed tasks can be re-run independently without reprocessing the entire workflow.
Can Airflow work with our existing tools?
Yes. Airflow has operators for most databases, cloud services, and APIs. Custom operators can connect to anything with an API or SDK.
What's the learning curve for our team?
Python developers can write DAGs within a week. We provide training and templates to accelerate adoption.
Ready to orchestrate your workflows?
Share your current scheduling challenges and we'll design an Airflow solution in a 30-minute call.
Subscribe and start making the most of every engagement.
