Airflow 2 is end of life. What should we do?
Apache ended support for Airflow 2 on 22 April 2026, so it gets no further fixes, security issues included. Airflow 3 is the supported line. The project recommends moving to the last 2.x release first and clearing deprecation warnings, then upgrading to 3. AceMQ plans and runs that move with your team and supports Airflow 2 and 3 in production while it happens.
Who answers when the Airflow scheduler stops scheduling, and how fast?
A named AceMQ senior engineer answers within 15 minutes, 24/7, when the scheduler stops or a business-critical DAG chain is stuck, on self-managed Airflow, MWAA, Cloud Composer or Astronomer. Lower severities get a first response within 1 hour, 4 hours or the next business day, and the engineer stays until the root cause is documented.
Should we use managed Airflow or self-host?
It depends on your Kubernetes maturity, compliance rules and how much control you need over versions and dependencies. Managed services remove scheduler and worker operations but not DAG design, pools or executor tuning. Self-hosting gives full control and puts upgrades on you. AceMQ models the cost and risk of both against your DAG count and task profile before recommending one.
CeleryExecutor or KubernetesExecutor: which do we need?
CeleryExecutor runs tasks on a fixed pool of long-lived workers, which is simpler to operate but static. KubernetesExecutor starts a pod per task, giving per-task isolation and scale to zero at the cost of pod start-up time and more Kubernetes tuning. AceMQ chooses from your task mix, start-up tolerance and scaling pattern, not by default.
Why do tasks sit in queued and never start?
Usually the task's queue name does not match what the workers listen on, every pool slot is taken, or zombie tasks hold executor slots after a worker crash. It is rarely the DAG code. AceMQ checks the queue, pool and slot counts first, then the scheduler logs, and removes the cause instead of restarting the scheduler and waiting for it to return.
Why does every DAG run late when only one DAG changed?
Almost always DAG parse time. A DAG file that calls an API or imports heavy libraries at the top level runs that code on every parse, which delays scheduling for every other DAG. AceMQ finds the offending file in the parse-time metrics and moves the work into a task, where it belongs.
Can you move us off cron or Pentaho onto Airflow?
Yes. AceMQ maps cron schedules or Pentaho job dependencies into explicit DAGs, adds the retries and alerting that cron never had, designs backfills for historical data, and runs the old and new schedulers in parallel until their output matches. Only then is the legacy scheduler switched off.
How does an Airflow engagement start, and how do you work with our team?
It starts with a scoping call booked in our reply within 1 hour, then a 1 to 2 week assessment of the scheduler, DAGs, executor and metadata database. Findings are written up within a week and changes are made alongside your engineers, DAG by DAG. You keep the runbooks, and ongoing support is optional.
Can we work with AceMQ on Apache Airflow without a long contract?
Yes. AceMQ works on Apache Airflow in two ways: ad hoc engagements for a defined piece of work, such as a health check, an upgrade or an incident, or an annual partner engagement that covers 24/7 support, proactive reviews and named engineers. Many teams start ad hoc and move to an annual partnership once the first engagement is done.
How much does AceMQ Apache Airflow support or consulting cost?
Every AceMQ proposal is tailored to your requirements: your Apache Airflow environment, the SLA you need, the hours of cover and the scope of work. Most contracts run $24,000–72,000 a year, depending on the SLA, the scope and how Apache Airflow is deployed, with aggressive discounts when several technologies share one contract. Request a quote and AceMQ replies within 1 hour.
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