Apache Airflow Support

24/7 Apache Airflow Support with a 15-Minute Emergency SLA

AceMQ supports Apache Airflow in production — schedulers that stop scheduling with no error, DAG parsing delays that quietly push back every downstream run, executor slot exhaustion from zombie tasks, and metadata database connection exhaustion under high concurrency. Every ticket reaches a named senior engineer who already knows your DAG topology.

Senior Apache Airflow engineers on call right now — 24/7/365
15 min emergency SLA24 /7 global coverage130 + enterprise customers26 + countries served

Trusted for mission-critical Apache Airflow by teams in finance, healthcare, defense, and telecom

Escalation Path

Your first hour of a Apache Airflow outage

Most vendors publish an SLA number. This is what actually happens, minute by minute, when you page a senior AceMQ engineer.

T+0

You page us

Phone, email, or Slack — any channel reaches the on-call senior engineer directly. No web form, no tier-1 queue.

T+15

Named engineer live

A senior engineer who already knows your environment joins a live bridge. Zero cold-start, no re-explaining your topology.

T+30

Root cause isolated

Direct broker access, log and metric review, and a working hypothesis with a rollback plan before we touch anything.

Post

Written RCA

Documented root cause, the fix applied, and the prevention steps — delivered after every P1, not just when asked.

Response Times

SLA tiers, contractually guaranteed

Every tier reaches a senior Apache Airflow engineer. There is no tier-1 triage layer to get through.

P1 — Emergency
15 min

Production down, messages not flowing, cluster or broker failure

P2 — Critical
1 hour

Severe degradation, rising error rates, approaching capacity limits

P3 — High
4 hours

Performance issues, configuration problems, non-critical failures

P4 — Standard
Next day

Questions, guidance, best practices, non-urgent improvements

Incident Triage

Apache Airflow problems we fix every week

These are real symptoms from real Apache Airflow production environments — and the first thing our engineers check when one comes in.

DAGs stop scheduling with no error in the UI or logs
What we check firstScheduler health and heartbeat, whether max_active_runs is saturated on the affected DAG, and whether executor slots are consumed by zombie tasks that never released cleanly after a worker crash.
Typical resolutionUnder 1 hour
Everything runs late, scheduling across the whole instance drifts
What we check firstDAG file parsing time in the scheduler logs. Heavy top-level imports or API calls executed at parse time — not inside a task — run on every scheduler heartbeat and delay scheduling for every DAG, not just the offending one.
Typical resolution1–3 hours
A task sits in 'queued' state and never starts
What we check firstExecutor and worker pool alignment — queue name mismatches between the task's assigned queue and the workers actually listening on it, or a worker pool with zero available slots.
Typical resolutionUnder 2 hours
XComs are enormous and the metadata DB is bloating or slow
What we check firstWhat's actually being passed through XCom versus external storage. Passing dataframes or large payloads through XCom instead of a reference to S3/GCS is the most common cause, and it degrades DB performance for every DAG, not just the culprit.
Typical resolution1–2 hours
Metadata database connections exhausted under load
What we check firstConnection pool sizing on the scheduler, webserver, and workers against DAG concurrency settings, plus whether Airflow's own connection pooling (SQLAlchemy pool_size) is configured for the actual DAG parallelism.
Typical resolution2–4 hours
Celery/Kubernetes executor workers crash and lose task state
What we check firstWorker pod resource limits against task memory usage, and whether task state is being tracked correctly through executor restarts. KubernetesExecutor pod evictions under resource pressure are a common silent cause.
Typical resolution2–4 hours
A DAG ran at the wrong time or ran twice
What we check firstschedule_interval and timezone configuration versus the DAG's actual start_date and catchup setting. Timezone-naive schedules and catchup=True combined with a backfill are the most frequent causes of unexpected double-runs.
Typical resolutionUnder 1 hour
Custom operators broke after upgrading to Airflow 2.x
What we check firstDAG serialization requirements and provider package compatibility. Airflow 2.x's DAG serialization model rejects certain dynamic patterns that worked fine under 1.x, and provider package version pins are a frequent silent break.
Typical resolution2–4 hours

Resolution times reflect typical Apache Airflow engagements under an active AceMQ support contract. Every P1 closes with a written root-cause analysis.

Not on the list? Tell us what's breaking
Support In Practice

Apache Airflow problems we've already solved

Representative engagements showing how these incidents get diagnosed and closed under an AceMQ support contract.

Remediation

Snowflake Query Spilling and Warehouse Queueing Remediation

Global Insurance Group

Resolving pipeline runtime blowouts caused by queries spilling to remote storage on undersized warehouses while concurrent jobs queued behind them.

SnowflakedbtAirflow
Read case study
Support

Snowflake Ingestion Pipeline Support

Retail Analytics Provider

Ongoing support for Snowpipe, stream, and task failures including stale streams past their retention window and silent partial-load conditions.

SnowflakeSnowpipeAirflow
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Assessment

Snowflake Clustering and Partition Pruning Assessment

Financial Market Data Provider

Assessment of clustering keys, micro-partition pruning, and table design on large tables where queries had begun scanning most of the data.

SnowflakedbtAirflow
Read case study
Remediation

Apache Airflow Zombie Task and Scheduling Stall Remediation

National Insurance Carrier

Restoring scheduling on Airflow deployments where zombie tasks hold executor slots and pools until nothing new gets queued.

Apache AirflowKubernetesCelery
Read case study
Support

Apache Airflow DAG Parse Time Support

Digital Retail Platform

Fixing scheduler delay caused by DAG files that make network or database calls at parse time, blocking every DAG in the deployment.

Apache AirflowKubernetesPostgreSQL
Read case study
Assessment

Apache Airflow Platform Assessment

Regional Utility Operator

Reviewing an Airflow deployment for reliability, DAG authoring practice, secrets handling, and upgrade readiness before it becomes unmaintainable.

Apache AirflowKubernetesPostgreSQL
Read case study
Assessment

Apache NiFi Dataflow Assessment

Life Sciences Technology Company

Assessing a NiFi estate for throughput headroom, provenance and audit coverage, security posture, and which flows belong on NiFi at all.

Apache NiFiApache KafkaApache Airflow
Read case study
What's Included

Everything in your Apache Airflow support contract

No add-on pricing for incidents. No per-ticket charges. One contract covers the whole surface.

Emergency Incident Response

The scheduler stops scheduling, or a business-critical DAG chain is stuck. A senior engineer joins a live bridge within 15 minutes with direct access to diagnose — not a ticket acknowledgement.

Root Cause Analysis

Every P1 closes with a written RCA: what failed, why, the fix applied, and the specific DAG, config, or infrastructure change that prevents recurrence.

Scheduler & Executor Tuning

max_active_runs, pool sizing, executor slot allocation, and DAG parse time reduction tuned against your actual DAG count and task concurrency.

Upgrade & Compatibility Advisory

Guidance on major version upgrades, provider package compatibility, and custom operator migration — including the DAG serialization changes that break naive 1.x-to-2.x upgrades.

Capacity & Metadata DB Reviews

Quarterly reviews of metadata database connection load, XCom growth, and worker resource headroom so scheduler performance doesn't degrade as DAG count grows.

Migration & Architecture Support

Executor migration (Celery to Kubernetes or vice versa), consolidating legacy cron and Pentaho-style batch jobs into Airflow, and DAG design review for teams scaling past a few hundred DAGs.

Anywhere You Run It

We support Apache Airflow wherever it's deployed

Cloud, Kubernetes, bare metal, hybrid, and air-gapped — including environments where you can't give us outbound network access.

Amazon MWAAGoogle Cloud ComposerAstronomerSelf-managed on KubernetesAWS (EC2, EKS)Google Cloud (GKE)Microsoft Azure (AKS)CeleryExecutor deploymentsKubernetesExecutor deploymentsAir-gapped / no outbound accessAirflow 2.x
Why AceMQ

What you get that you don't get elsewhere

Named Engineers, Zero Cold Start

The same senior engineers stay on your account. They know your DAG topology, your executor setup, and which pipelines are fragile — so a P1 starts with diagnosis, not twenty minutes of explaining your environment.

No Tier-1 Triage Layer

You reach a senior Airflow engineer directly by phone, email, or Slack. No help desk collecting information to pass along, no escalation approval process standing between you and someone who can fix it.

Orchestration and the Systems Underneath

Most Airflow incidents are metadata database, executor infrastructure, or DAG design problems wearing an Airflow error. We diagnose across the whole stack, not just the scheduler UI.

Genuine Follow-the-Sun Coverage

Engineers across 26+ countries and every time zone. Your overnight batch failure is someone's mid-afternoon — no overnight skeleton crew, no waiting for a region to wake up.

Proactive, Not Just Reactive

Quarterly health checks on DAG parse time and metadata DB load, plus shared intelligence across our support base. When a provider package bug surfaces on one customer's DAGs, every affected customer hears about it before it reaches their production.

We Read Your DAGs, Not Just Your Logs

Airflow failures are frequently caused by DAG authoring patterns — top-level API calls, unbounded XCom, missing idempotency — that logs alone won't reveal. We review the actual DAG code when that's where the problem lives.

FAQ

Apache Airflow support questions

Your DAGs Shouldn't Stop Running Without an Alert

Whether you need emergency response tonight or a support contract that catches scheduler drift before it becomes an outage, AceMQ staffs every engagement with a named senior Airflow engineer. Support quotes returned within 24 hours.

Get in Touch

Talk to a Support Expert

Send us a message and we'll follow up within one business day — or book a free 30-min consultation directly.

305-204-2607
info@acemq.com
66 W. Flagler St. 9th Floor
Miami, FL 33130

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