Airflow deployments degrade gradually. DAG count grows, authoring conventions diverge between teams, the metadata database is never maintained, and the version falls far enough behind that upgrading becomes its own project. AceMQ assesses the deployment and returns a prioritized remediation plan.
The problems are rarely reported as Airflow problems — they surface as unreliable pipelines and slow delivery. Underneath, tasks are not idempotent so retries corrupt data, credentials are stored in Variables instead of a secrets backend, the metadata database has grown to hundreds of gigabytes of task instance history, sensor tasks occupy worker slots for hours, and the deployment is several minor versions behind with no upgrade path rehearsed.
Apache Airflow 2.x deployments on Kubernetes, VMs, or managed services, orchestrating Spark, dbt, warehouse, and external API workloads.
AceMQ reviews the deployment across four axes — infrastructure and configuration, DAG authoring practice, security and secrets handling, and upgrade readiness — against the deployment's own history rather than a generic checklist. Findings are ranked by the operational risk they carry and the effort required to close them.
Customers get a ranked remediation backlog covering reliability, security, and upgrade readiness, with the highest-risk items — usually non-idempotent tasks and credentials in plain Variables — identified explicitly rather than buried in a general recommendation.
Moving from Celery to the Kubernetes executor, or the reverse, with a sizing model and deployment design that matches the workload profile.
Fixing scheduler delay caused by DAG files that make network or database calls at parse time, blocking every DAG in the deployment.
Whether you need architecture advisory, 24/7 support, or full managed services, AceMQ has the expertise to help.