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24/7 Managed Operations • Cloud-Agnostic
Managed Apache Airflow for Production Data Pipelines
Stop wrestling with Airflow infrastructure. Our fully managed Apache Airflow service handles architecture, monitoring, upgrades, DAG deployment, and 24/7 support—so your data team can focus on building pipelines that matter.
4.9★ Clutch
ISO 27001
200+ Teams
In 50 CLUSTERS+
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 Apache Airflow 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 pipelines, and version upgrades require deep expertise that most data teams don't have in-house.
Our managed Apache Airflow service eliminates this operational overhead. We handle architecture design, deployment on Kubernetes, monitoring with Prometheus, automated backups, security hardening, and 24/7 incident response—on AWS, Azure, GCP, or on-premise infrastructure.
Unlike cloud-vendor solutions like AWS MWAA or Google Cloud Composer that lock you into a single cloud with limited customization, our airflow managed service gives you full executor flexibility, custom Docker image support, and direct access to Airflow specialists who understand your DAGs. Whether you're migrating from legacy schedulers, upgrading to Airflow 3.x, or need airflow as a service for your growing data platform, we design and operate environments that scale with your pipeline complexity.
Why Choose Our Managed Apache Airflow Service
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 Apache Airflow Services
End-to-end Airflow consulting and managed operations
Airflow Architecture & Design
Design production-grade Airflow architectures optimized for your workload—choosing the right executor, scheduler configuration, metadata database, and infrastructure topology.
- Executor selection (Celery/K8s)
- Scheduler & worker sizing
- HA & fault-tolerant design
- Multi-tenant DAG isolation
Airflow Migration & Upgrades
Migrate from Cron, Luigi, Oozie, or legacy schedulers to Apache Airflow. Upgrade from Airflow 1.x to 2.x or 3.x with zero pipeline disruption.
- Airflow 1.x to 2.x/3.x migration
- Legacy scheduler migration
- DAG refactoring & testing
- Zero-downtime cutover
Performance Optimization
Tune Airflow for faster DAG parsing, reduced scheduler latency, and optimized worker utilization. Fix task queuing bottlenecks and resource contention.
- DAG parsing optimization
- Scheduler tuning & profiling
- Worker concurrency tuning
- Database query optimization
24/7 Managed Operations
Fully managed Apache Airflow operations with proactive monitoring, automated backups, version upgrades, security patching, and SLA-backed incident response.
- 24/7 monitoring & alerting
- Automated backups & recovery
- Zero-downtime upgrades
- 99.9% uptime SLA
Airflow on Kubernetes
Deploy and manage Airflow on Kubernetes with KubernetesExecutor, Helm charts, auto-scaling workers, and GitOps-driven DAG deployment for cloud-native operations.
- KubernetesExecutor setup
- Helm chart deployment
- Worker pod auto-scaling
- GitOps DAG CI/CD
Cloud Airflow Management
Expert management of AWS MWAA, Google Cloud Composer, and Astronomer/Astro environments. Cost optimization, security hardening, and operational best practices.
- AWS MWAA management
- Cloud Composer optimization
- Astronomer/Astro support
- Cloud cost optimization
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 & After Our Managed Airflow Service
The transformation data teams experience when we manage their Airflow infrastructure
Without DevOps
- 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
With DevOps Consulting
- Proactively tuned scheduler with zero DAG parsing delays
- Real-time alerting with <5 min incident response
- Zero-downtime rolling upgrades with rollback plans
- Automated daily backups with validated disaster recovery
- Auto-scaling workers that handle traffic spikes
- Data engineers focused on building pipelines
Managed Airflow: Tasrie vs Cloud Alternatives
See how our managed Airflow service compares to cloud-native options
| Feature | Tasrie Managed | AWS MWAA | Cloud Composer | Astronomer |
|---|---|---|---|---|
| Cloud Support | Any cloud + on-prem | AWS only | GCP only | Multi-cloud |
| Executor Options | All executors | Celery only | Limited | All executors |
| Docker Customization | Full access | requirements.txt only | Limited | Full access |
| Pipeline Expertise | DAG + infra support | Infrastructure only | Infrastructure only | Airflow platform |
| Monitoring | Prometheus/Grafana | CloudWatch | Stackdriver | Built-in + custom |
| Vendor Lock-in | None | High (AWS) | High (GCP) | Low |
How Our Managed Apache Airflow Service Works
A structured approach from assessment to fully managed operations
- 1
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.
- 2
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.
- 3
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.
- 4
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 Our Managed Airflow Service
Operational expertise backed by data infrastructure experience
Deep Airflow Expertise
Engineers experienced with executors, schedulers, operators, and DAG patterns at scale
Cloud-Agnostic Operations
Kubernetes-native deployments on AWS, Azure, GCP, or on-premise—no lock-in
Pipeline-Aware Support
We understand your DAGs and data flows, not just the infrastructure underneath
ISO 27001 Certified
Enterprise-grade security and compliance standards for sensitive data pipelines
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.
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 Apache Airflow FAQs
Common questions about our managed Airflow service
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.
Can you upgrade our Airflow from 1.x to 2.x or 3.x?
Yes. Migrating between major Airflow versions involves DAG API changes, import path updates, operator refactoring, and metadata database migration. We handle the full upgrade with proper testing, rollback plans, and zero-downtime cutover strategies. We also support upgrading to Airflow 3.x with its new features like the asset-based scheduling.
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 Apache Airflow?
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.
-
Reliability
Improve change success rate and MTTR.
-
Cost control
Kubernetes/GitOps patterns that scale efficiently.
No sales spam—just a short conversation to see if we can help.
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