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24/7 Managed Operations • Cloud-Agnostic
Managed Airflow Services
Stop wrestling with Airflow infrastructure. Our managed Airflow services handle Apache Airflow architecture, monitoring, Airflow 3.x upgrades, DAG deployment, backups, and 24/7 support across AWS MWAA, Cloud Composer, Kubernetes, Azure, GCP, and on-prem environments.
Clutch
ISO 27001
In 4 WEEKS
You'll Have
DAGs that run reliably in production
Zero-downtime Airflow upgrades
Monitoring you can trust at 3 AM
Engineers focused on pipelines, not ops
50+
Production Clusters
24/7
Managed Support
99.9%
Uptime SLA
Trusted by data-driven organizations
Managed Airflow Services for Data Teams That Ship
Apache Airflow is the industry standard for data pipeline orchestration, used by Airbnb, Spotify, and thousands of data teams worldwide. But running Airflow in production is operationally demanding: scheduler tuning, worker scaling, metadata database maintenance, DAG deployment, and version upgrades require deep expertise that most data teams don't have in-house.
Our managed Airflow services eliminate this operational overhead for Apache Airflow teams. We handle architecture design, deployment on Kubernetes, monitoring with Prometheus, automated backups, security hardening, Airflow 3.2 upgrades, and 24/7 incident response on AWS, Azure, GCP, or on-premise infrastructure.
Unlike cloud-vendor managed Airflow options like AWS MWAA, Google Cloud Composer, and Azure Data Factory Managed Airflow that lock you into a single cloud with limited customization, our fully managed Airflow service gives you full executor flexibility, custom Docker image support, and direct access to Airflow consulting specialists who understand your DAGs. Whether you need an MWAA alternative, a Cloud Composer alternative, are migrating from legacy schedulers, upgrading to Airflow 3.x, or need Airflow as a service for your growing data platform, we design and operate managed workflow orchestration environments that scale with your pipeline complexity.
Why Choose Our Managed Airflow Services
Production-grade Airflow without the operational complexity
Cloud-Agnostic
Run Airflow on AWS, Azure, GCP, on-premise, or Kubernetes. No vendor lock-in—full control over your infrastructure and data.
24/7 Expert Support
Round-the-clock monitoring and support from Airflow specialists. Proactive alerting catches pipeline failures before they impact downstream systems.
99.9% Uptime SLA
SLA-backed availability with high-availability scheduler, automated failover, and tested disaster recovery procedures for your pipelines.
Full Executor Flexibility
Use any Airflow executor—CeleryExecutor, KubernetesExecutor, or CeleryKubernetesExecutor. No restrictions like MWAA or Cloud Composer impose.
Zero-Downtime Upgrades
Rolling Airflow upgrades, DAG migrations, and infrastructure scaling without impacting running pipelines or data delivery SLAs.
Pipeline Expertise
Engineers who've built and operated Airflow at scale—not just infrastructure generalists. We understand DAGs, operators, and data pipeline patterns.
Our Managed Airflow Services
End-to-end Apache Airflow consulting, managed workflow orchestration, and production operations
Airflow Use Cases We Manage
From ETL pipelines to ML workflows—managed Airflow for every data workload
ETL/ELT Data Pipelines
Extract, transform, and load data across databases, APIs, data lakes, and warehouses like Snowflake, BigQuery, and Redshift with Airflow DAGs.
Data Warehouse Orchestration
Orchestrate dbt models, Snowflake tasks, BigQuery jobs, and warehouse loading workflows with dependency management and SLA monitoring.
ML & AI Pipeline Orchestration
Automate model training, feature engineering, data validation, and model deployment workflows. Integrate with SageMaker, Vertex AI, and MLflow.
Data Integration & Sync
Synchronize data across SaaS applications, internal databases, and third-party APIs. Incremental loads, change data capture, and reconciliation.
Compliance & Reporting
Automate regulatory reporting, data quality checks, audit trail generation, and compliance workflows with scheduled and event-driven DAGs.
Big Data Processing
Orchestrate Apache Spark jobs, EMR clusters, Dataproc workflows, and batch processing pipelines at scale with Airflow operators.
Before and After Our Managed Airflow Services
The transformation data teams experience when we manage their Airflow infrastructure
- Hours spent debugging scheduler bottlenecks
- DAG failures discovered hours later
- Manual upgrades causing pipeline downtime
- No backups or untested recovery
- Workers crashing under peak load
- Data engineers managing infrastructure
Tap to see how things change
Managed Airflow Services: Tasrie IT Services vs MWAA, Cloud Composer, Azure, and Astronomer
See how our managed Airflow services compare to AWS MWAA, Google Cloud Composer, Azure Data Factory Managed Airflow, and Astronomer Astro
| Feature | Tasrie IT Services | AWS MWAA | Cloud Composer | Azure Managed Airflow | Astronomer |
|---|---|---|---|---|---|
| Cloud Support | Any cloud + on-prem | AWS only | GCP only | Azure only | Multi-cloud |
| Executor Options | All executors | Celery only | Limited | Celery only | All executors |
| Docker Customization | Full access | requirements.txt only | Limited | requirements.txt only | Full access |
| Pipeline Expertise | DAG + infra support | Infrastructure only | Infrastructure only | Infrastructure only | Airflow platform |
| Monitoring | Prometheus/Grafana | CloudWatch | Cloud Monitoring | Azure Monitor | Built-in + custom |
| Vendor Lock-in | None | High (AWS) | High (GCP) | High (Azure) | Low |
How Our Managed Airflow Services Work
A structured approach from assessment to fully managed operations
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Assessment & Planning
We analyze your existing data pipelines, DAG complexity, scheduler performance, and growth projections. We evaluate your Airflow version, executor requirements, and infrastructure to design the right managed architecture.
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Deployment & Migration
We provision Airflow environments on your chosen infrastructure, configure executors and workers, set up monitoring with Prometheus and Grafana, and migrate your existing DAGs with zero pipeline disruption.
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Optimization & Tuning
We optimize scheduler configuration, worker concurrency, DAG parsing performance, and metadata database queries. We right-size infrastructure resources and implement cost optimization to reduce your compute spend.
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Managed Operations
Ongoing 24/7 monitoring, automated backups, security patches, version upgrades, capacity planning, and incident response. Your data team focuses on building pipelines while we handle Airflow operations.
Why Choose Tasrie IT Services for Managed Airflow
Operational expertise backed by data infrastructure experience
What makes us different
We're not a typical consultancy. Here's why that matters.
No vendor bias
No commissions, no referral incentives, no behind-the-scenes partnerships. We stay neutral so you get the best option — not the one that pays.
Engineering-first, not sales-first
All engagements are led by senior engineers, not sales reps. Conversations are technical, pragmatic, and honest.
Technology chosen on merit
We help you pick tech that is reliable, scalable, and cost-efficient — not whatever is hyped or expensive.
Built around your real needs
We design solutions based on your business context, your team, and your constraints — not generic slide decks.
Managed Apache Airflow Case Studies
Real outcomes from data teams running production Airflow with us
Trusted by Data Teams
See what our clients say about our managed data infrastructure services
"Their team helped us improve how we develop and release our software. Automated processes made our releases faster and more dependable. Tasrie modernized our IT setup, making it flexible and cost-effective. The long-term benefits far outweighed the initial challenges. Thanks to Tasrie IT Services, we provide better youth sports programs to our NYC community."
Anthony Treyman
Kids in the Game, New York
"Tasrie IT Services successfully restored and migrated our servers to prevent ransomware attacks. Their team was responsive and timely throughout the engagement."
Rose Wang
Operations Lead
"Tasrie IT has been an incredible partner in transforming our investment management. Their Kubernetes scalability and automated CI/CD pipeline revolutionized our trading bot performance. Faster releases, better decisions, and more innovation."
Shahid Ahmed
CEO, Jupiter Investments
"Their team deeply understood our industry and integrated seamlessly with our internal teams. Excellent communication, proactive problem-solving, and consistently on-time delivery."
Justin Garvin
MediaRise
"The changes Tasrie made had major benefits. Fewer outages, faster updates, and improved customer experience. Plus we saved a good amount on costs."
Nora Motaweh
Burbery
Our Industry Recognition and Awards
Discover our commitment to excellence through industry recognition and awards that highlight our expertise in driving DevOps success.
Managed Airflow Services FAQs
Common questions about our managed Airflow services for Apache Airflow
What is a managed Apache Airflow service?
A managed Apache Airflow service handles the operational burden of running Airflow in production—including installation, configuration, monitoring, DAG deployment, backups, upgrades, and scaling. Our managed Airflow service gives you production-grade pipeline orchestration without needing in-house Airflow infrastructure expertise, backed by 24/7 support and SLA guarantees.
How is your managed Airflow service different from AWS MWAA or Cloud Composer?
AWS MWAA and Google Cloud Composer are cloud-vendor-specific managed services with limited customization—restricted executor options, no Docker image access, and vendor lock-in. Our managed Airflow service is cloud-agnostic (AWS, Azure, GCP, on-premise), offers full executor flexibility (Celery, Kubernetes, CeleryKubernetes), custom Docker image support, and direct access to Airflow specialists who understand your DAGs and pipelines.
Can you manage Airflow running on AWS MWAA or Cloud Composer?
Yes. We manage Airflow across all deployment models—self-hosted on Kubernetes, AWS MWAA, Google Cloud Composer, and Astronomer/Astro. For MWAA and Composer, we handle DAG deployment, monitoring, cost optimization, security hardening, and troubleshooting within the platform's constraints.
Which Airflow executors do you support?
We support all Apache Airflow executors: LocalExecutor for small deployments, CeleryExecutor for distributed task execution, KubernetesExecutor for dynamic pod-based scaling, and CeleryKubernetesExecutor for hybrid workloads. We select the right executor based on your DAG complexity, scale, and infrastructure.
Can you migrate us from Cron jobs, Luigi, or Oozie to Airflow?
Yes. We specialize in migrating from legacy orchestrators (Cron, Luigi, Oozie, custom scripts) to Apache Airflow. Our migration process includes workflow mapping, DAG development, parallel running, testing, and cutover with zero data pipeline disruption.
Do you support Apache Airflow 3.x and 3.2 upgrades?
Yes. We support upgrades to Apache Airflow 3.x and the latest Airflow 3.2 release, which adds asset-based scheduling, improved developer productivity, and new data-aware orchestration features. Migrating between major Airflow versions involves DAG API changes, import path updates, operator refactoring, and metadata database migration. We handle the full Airflow 3.x upgrade with proper testing, rollback plans, and zero-downtime cutover strategies, whether you run self-hosted Airflow, AWS MWAA, Google Cloud Managed Service for Apache Airflow, or Azure Data Factory Managed Airflow.
What is the best managed Airflow service in 2026?
The best managed Airflow service depends on your stack and constraints. AWS MWAA suits AWS-only teams that can live with Celery-only execution and requirements.txt limits. Google Cloud Managed Service for Apache Airflow (Cloud Composer) is the natural choice for GCP-native data stacks. Azure Data Factory Managed Airflow fits Microsoft shops. Astronomer Astro is the strongest SaaS option for teams that want a managed product. Our managed Airflow services are the right fit when you need cloud-agnostic deployment, full executor flexibility, custom Docker images, and an Airflow consulting partner who supports both your infrastructure and your DAGs, not just the platform.
How do you monitor managed Airflow environments?
We deploy comprehensive monitoring using Prometheus and Grafana with Airflow-specific dashboards. We track DAG run success rates, task duration, scheduler latency, worker utilization, metadata database performance, and queue depth. Custom alerts ensure proactive detection of pipeline failures, SLA misses, and infrastructure issues.
What does your managed Airflow service cost?
Managed Airflow costs depend on cluster size, DAG complexity, number of environments, and support level. Our service starts with a consultation to right-size your deployment. We provide transparent pricing with no hidden fees—typical engagements include a setup phase followed by monthly managed operations.
Do you support Airflow integration with dbt, Spark, and data warehouses?
Yes. We set up and manage Airflow integrations with dbt Core/Cloud, Apache Spark, Snowflake, BigQuery, Redshift, ClickHouse, Kafka, and other data tools. We build custom operators and connections as needed for your data stack.
How do you handle Airflow backups and disaster recovery?
We implement automated daily backups of the Airflow metadata database, DAG definitions, connections, variables, and pools. Our disaster recovery includes cross-region replication, point-in-time recovery, and regular DR testing to ensure pipeline continuity.
Can you manage multiple Airflow environments (dev, staging, production)?
Yes. We manage multi-environment Airflow setups with proper isolation, GitOps-driven DAG promotion (dev → staging → production), environment-specific configurations, and access controls. This ensures safe DAG testing before production deployment.
What security measures do you implement for managed Airflow?
We harden Airflow deployments with RBAC (Role-Based Access Control), LDAP/OAuth integration, encrypted connections and variables, TLS encryption in transit, network isolation, secrets backend integration (AWS Secrets Manager, HashiCorp Vault), and audit logging for compliance.
Ready to Get Started with Managed Airflow Services?
Get a free assessment of your Airflow environment. We'll recommend the right architecture and provide a detailed proposal within 48 hours.
"We build relationships, not just technology."
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Faster delivery
Reduce lead time and increase deploy frequency.
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Reliability
Improve change success rate and MTTR.
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Cost control
Kubernetes/GitOps patterns that scale efficiently.
No sales spam—just a short conversation to see if we can help.
Submission received
Thanks! We'll be in touch shortly.
Apache Airflow Articles and Guides
In-depth guides on Airflow upgrades, migrations, and managed operations
Engineering 20 min
Managed Airflow Services Compared: 6 We Tested in 2026
We tested 6 managed Apache Airflow services in production. Real costs, pros, cons, and when to pick MWAA, Cloud Composer, Astronomer, or run Airflow yourself.
Data-Engineering DevOps
Amjad Syed
Read article
Engineering 12 min
Airflow 2 End of Life: We Migrated 200+ DAGs Before the Deadline (What You Must Know)
Apache Airflow 2 reaches end of life on April 22, 2026. No more security patches, bug fixes, or provider updates. Here is what breaks, compliance risks, and your migration options.
Data-Engineering DevOps
Amjad Syed
Read article
Engineering 14 min
We Upgraded Airflow 2.8 to 3.1 on Kubernetes. Here Is What Actually Changed
How we migrated Apache Airflow from 2.8 to 3.1 on EKS using a green field deployment. Import path changes, variable export, git-sync, LDAP gotchas, and the full Kubernetes manifest breakdown.
Data-Engineering Kubernetes
Amjad Syed
Read article
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