An honest read on whether your Airflow deployment will survive its next growth phase
Kubernetes
PostgreSQL
Apache SparkCustomers 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 — identi…
Overview
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.
Challenge
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.
Environment
Apache Airflow 2.x deployments on Kubernetes, VMs, or managed services, orchestrating Spark, dbt, warehouse, and external API workloads.
Approach
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.
Solution
- 1Scheduler, executor, and metadata database configuration review against measured DAG and task volume
- 2DAG authoring audit covering idempotency, retry safety, top-level code, and dependency structure across teams
- 3Secrets and connection handling review, including credentials stored in Variables that belong in a secrets backend
- 4Metadata database growth and maintenance assessment with retention and archival recommendations
- 5Sensor usage review, converting blocking sensors to deferrable operators where worker slots are being consumed by waiting
- 6Version currency and upgrade path analysis, including provider package compatibility and a rehearsed upgrade plan
Outcome
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.
Technologies
KubernetesPostgreSQLApache SparkRelated Use Cases
Apache Airflow Executor Migration and Platform Design
Moving from Celery to the Kubernetes executor, or the reverse, with a sizing model and deployment design that matches the workload profile.
Apache Airflow DAG Parse Time Support
Fixing scheduler delay caused by DAG files that make network or database calls at parse time, blocking every DAG in the deployment.
Ready for a Apache Airflow Health Check?
AceMQ's senior Apache Airflow engineers have handled this exact type of engagement before. Whether you need architectural guidance, hands-on remediation, or an ongoing managed partnership, we're ready to help.