Apache Airflow Support Packages
10 days resolution time
2 days resolution time
6 days resolution time
2 hours resolution time (via urgent ticket)
10 days resolution time
2 days resolution time
6 days resolution time
2 hours resolution time (via urgent ticket)
10 days resolution time
2 days resolution time
6 days resolution time
2 hours resolution time (24/7 included)
Why Ksolves is a Trusted Choice of Global
Teams for Apache Airflow Support?
Ksolves delivers expert Apache Airflow support services to keep your workflows efficient, secure, and
high-performing with professionals.
or planning an Airflow 3.x upgrade, our Apache Airflow implementation
experts have you covered at every stage of the orchestration lifecycle.
End-to-end Apache Airflow implementation from environment setup, architecture design, and DAG authoring to production deployment. Our Apache Airflow implementation experts ensure a robust, scalable foundation tailored to your data stack, whether on-prem, cloud, or hybrid.
Design and refine DAGs for faster execution, minimal latency, and proactive issue detection. We analyse task dependencies, eliminate bottlenecks, implement dynamic DAG generation, configure DAG bundles for version-controlled deployments, and apply Airflow coding guidelines across all your workflows.
Optimize CeleryExecutor, KubernetesExecutor, LocalExecutor, and the Airflow 3.x EdgeExecutor for peak performance and reliability. Our Airflow services team fine-tunes parallelism, concurrency limits, task slots, and resource allocation to eliminate queueing delays and missed schedules.
Perform zero-downtime Airflow version upgrades, including migrations to Airflow 3.x with Task SDK compatibility checks, DAG versioning enablement, and provider package validation. We handle migrations from legacy orchestrators and managed services like MWAA, Cloud Composer, and Astronomer with rollback safeguards at every stage.
Connect Airflow effortlessly with Kafka, Spark, Hive, AWS, GCP, Azure, Snowflake, dbt, and 50+ platforms via official provider packages. We build custom operators, sensors, hooks, and plugins, and configure event-driven scheduling so pipelines trigger on real data events, not just cron schedules.
Enable real-time pipeline visibility with Prometheus, Grafana, and the ELK stack, backed by intelligent alerting. Get notified before failures cascade, with custom dashboards, SLA breach alerts, and automated incident escalation workflows.
Build bespoke operators, sensors, and plugins to meet your unique orchestration requirements. Our Apache Airflow experts craft production-grade, reusable components with full unit test coverage and CI/CD pipeline integration.
Airflow Implementation Services
End-to-end Apache Airflow implementation from environment setup, architecture design, and DAG authoring to production deployment. Our Apache Airflow implementation experts ensure a robust, scalable foundation tailored to your data stack, whether on-prem, cloud, or hybrid.
DAG Optimization & Tuning
Design and refine DAGs for faster execution, minimal latency, and proactive issue detection. We analyse task dependencies, eliminate bottlenecks, implement dynamic DAG generation, configure DAG bundles for version-controlled deployments, and apply Airflow coding guidelines across all your workflows.
Scheduler & Executor Tuning
Optimize CeleryExecutor, KubernetesExecutor, LocalExecutor, and the Airflow 3.x EdgeExecutor for peak performance and reliability. Our Airflow services team fine-tunes parallelism, concurrency limits, task slots, and resource allocation to eliminate queueing delays and missed schedules.
Upgrades & Migrations
Perform zero-downtime Airflow version upgrades, including migrations to Airflow 3.x with Task SDK compatibility checks, DAG versioning enablement, and provider package validation. We handle migrations from legacy orchestrators and managed services like MWAA, Cloud Composer, and Astronomer with rollback safeguards at every stage.
Seamless Integrations
Connect Airflow effortlessly with Kafka, Spark, Hive, AWS, GCP, Azure, Snowflake, dbt, and 50+ platforms via official provider packages. We build custom operators, sensors, hooks, and plugins, and configure event-driven scheduling so pipelines trigger on real data events, not just cron schedules.
Monitoring & Alerting
Enable real-time pipeline visibility with Prometheus, Grafana, and the ELK stack, backed by intelligent alerting. Get notified before failures cascade, with custom dashboards, SLA breach alerts, and automated incident escalation workflows.
Custom Operator & Plugin Development
Build bespoke operators, sensors, and plugins to meet your unique orchestration requirements. Our Apache Airflow experts craft production-grade, reusable components with full unit test coverage and CI/CD pipeline integration.
Our Airflow Support Engagement Process
We follow a structured and transparent approach from first contact to continuous optimisation, so you always know what to expect from our airflow support services team.
Discovery & Audit
Assess your Airflow environment, DAG structure, and pain points to build a clear improvement roadmap.
Plan & Scope
Define the support model, SLAs, integrations, and delivery milestones aligned to your requirements.
Onboarding
Set up monitoring, establish support channels, document your environment, and meet your dedicated expert team.
Active Support
24x7 incident resolution, proactive optimisation, and regular reporting within agreed SLA windows.
Continuous Improvement
Quarterly reviews, upgrade planning, and ongoing airflow improvement services to keep your stack ahead of demand.
What does Airflow implementation support include?
Our Airflow implementation support covers environment setup, architecture design, DAG development, executor configuration, monitoring setup, integration with your existing data stack, and knowledge transfer to your team, end-to-end.
Can you help with upgrades and version migrations?
Yes. Our Airflow services team handles zero-downtime version upgrades, including migrations to Airflow 3.x. We run Task SDK compatibility checks, validate DAG versioning behaviour, confirm provider package compatibility, and prepare rollback plans, covering migrations from MWAA, Cloud Composer, Astronomer, and self-hosted Airflow 2.x environments.
Do you provide support for stalled DAGs and scheduling failures?
Absolutely. Whether it’s stalled DAGs, delayed schedules, failing integrations, or scheduler bottlenecks, our Apache Airflow experts are available 24×7 to diagnose and resolve complex orchestration issues quickly.
Can you help scale our Airflow deployment?
Yes. Our team assists with auto-scaling executor configurations, Kubernetes-based deployments, resource pool management, and infrastructure right-sizing to handle growing workload demands without performance degradation.
What integrations do your Apache Airflow experts support?
We support integrations with Kafka, Spark, Hive, Snowflake, dbt, AWS (S3, Glue, Redshift), GCP (BigQuery, Dataproc), Azure (ADF, ADLS), and many more, including custom operators for proprietary internal systems.
Do your Airflow improvement services include team training?
Yes. Professional support includes Airflow best practices training for DAG design, monitoring, cluster operations, and troubleshooting, ensuring your internal team can operate the environment confidently alongside our experts.