01
Emergency Incident Response
Production pipelines down, jobs failing cluster-wide, or a workspace that won't provision. A senior engineer joins a live bridge within 15 minutes with direct access to diagnose.
Databricks Support
24/7 Databricks Support with a 15-Minute Emergency SLA
Last reviewed by Scott Sternloff
AceMQ supports Databricks in production across AWS, Azure, and GCP — job cluster cold starts, Delta small-file compaction, skewed joins, and Unity Catalog migrations off the legacy Hive metastore. Every ticket reaches a named senior engineer who already knows your workspace.
Support quotes answered within 1 hour, by a senior engineer.
- min emergency SLA
- 15
- across Americas, EMEA, APAC
- 24/7
- enterprise customers
- 160+
- countries served
- 26+
Planning a build, upgrade or migration instead? See AceMQ Databricks consulting and services.
Customers
Partners
Incident Triage
Databricks problems we fix every week
These are real symptoms from real Databricks production environments — and the first thing our engineers check when one comes in.
| Symptom you're seeing | What we check first | Typical resolution |
|---|---|---|
| Job clusters spend most of their billed time on cold start | Autoscaling min/max worker settings against actual job runtime. A min_workers of 0 forces a full cluster provision on every scheduled run — often the majority of the DBU bill for short jobs. | 1–2 hours |
| Delta table writes slowing down, thousands of tiny files piling up | Average file size via DESCRIBE DETAIL and OPTIMIZE history. High-frequency streaming writes without auto-compaction or a scheduled OPTIMIZE job are the standard cause. | 1–3 hours |
| Driver returns 'not responsive' or crashes with OOM | Notebook and job code for collect() or toPandas() calls pulling a full dataset to the driver node, plus broadcast join thresholds against actual table size. | Under 2 hours |
| One executor runs 10x longer than every other task in the stage | Spark UI stage detail for partition size skew on the join key. Salting the key or enabling adaptive query execution's skew join handling is the usual fix. | 1–3 hours |
| Unity Catalog permission errors after a Hive metastore migration | External location grants, metastore assignment, and which tables were never registered into Unity Catalog — lineage gaps trace back to this almost every time. | 2–4 hours |
| DBU spend blows past budget with no workload change | Cluster event logs for all-purpose clusters left running versus job clusters, and auto-termination timeout settings. Idle all-purpose clusters are the most common line item. | Same day |
| VACUUM removes files a running time-travel query still needs | Retention interval configuration against in-flight time-travel queries and any concurrent OPTIMIZE jobs touching the same table. | Under 2 hours |
| Photon isn't engaging on a workload that should qualify | Cluster runtime version and whether the workload's operators are Photon-supported — a single UDF in the query plan disqualifies the whole stage from Photon acceleration. | 1–2 hours |
Resolution times reflect typical Databricks engagements under an active AceMQ support contract. Every P1 closes with a written root-cause analysis.
What's Included
Everything in your Databricks support contract
No add-on pricing for incidents. No per-ticket charges. One contract covers the whole surface.
- 02
Root Cause Analysis
Every P1 closes with a written RCA: what failed, why, the fix applied, and the specific configuration change that prevents recurrence.
- 03
Performance Tuning
Cluster sizing, autoscaling policy, Photon eligibility, and Delta table layout tuned against your actual job runtime and DBU budget — not generic best practices.
- 04
Cost Governance
Cluster policy review, job-vs-all-purpose cluster audits, and quarterly DBU spend analysis so runaway clusters get caught before the invoice does.
- 05
CVE & Patch Advisory
Proactive alerts for security issues affecting your exact Databricks Runtime version, with tested upgrade paths that account for job compatibility.
- 06
Migration & Unity Catalog Support
Hive metastore to Unity Catalog migration planning, workspace-to-workspace moves, and lineage remediation for tables that fell through the cracks.
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 cluster policies, your Delta table layout, and your job schedules — so a P1 starts with diagnosis, not you re-explaining your workspace.
No Tier-1 Triage Layer
You reach a senior Databricks engineer directly by phone, email, or Slack. No help desk collecting information, no escalation approval process between you and someone who can fix it.
The Whole Lakehouse Triangle
We support Databricks, Snowflake, and Starburst as a connected stack, not in isolation — which matters when the actual problem is where data lives, not which engine is running the query.
Cost Discipline, Not Just Uptime
Quarterly cluster policy and DBU spend reviews are standard, not an upsell. Most Databricks cost overruns are idle all-purpose clusters nobody is watching, and we watch.
Genuine Follow-the-Sun Coverage
Engineers across 26+ countries and every time zone. Your 3am pipeline failure is someone's mid-afternoon — no overnight skeleton crew.
Full-Stack, Not Just the Cluster
Databricks problems are often cloud IAM, network, or storage problems wearing a Spark stack trace. We diagnose across the cloud provider, the JVM, and the lakehouse layer.
We support Databricks wherever it's deployed
Cloud, Kubernetes, bare metal, hybrid, and air-gapped — including environments where you can't give us outbound network access.
- Databricks on AWS
- Databricks on Azure
- Databricks on GCP
- Unity Catalog
- Delta Lake
- Databricks SQL Warehouses
- Job clusters & all-purpose clusters
- MLflow
- VPC / VNet peering & PrivateLink
- Hybrid cloud
- Air-gapped / restricted network
Escalation Path
Your first hour of a Databricks outage
Every tier reaches a senior Databricks engineer. There is no tier-1 triage layer to get through.
- 15 minfirst responseEmergencyProduction down, messages not flowing, queue manager failure
- 1 hourfirst responseCriticalSevere degradation, high error rates, approaching capacity
- 4 hoursfirst responseHighPerformance issues, configuration problems, non-critical failures
- Next Dayfirst responseStandardQuestions, guidance, best practices, non-urgent improvements
P1 escalation path
A replay of one P1, from the first page to the written root-cause analysis
T+Elapsed15:00
15-minute P1 response, 24/7.
A response time, not a resolution time.
- T+00:00Paged
P1 opened, on-call engineer paged
Phone, email or Slack reaches the on-call senior engineer directly, whether the incident is in RabbitMQ, Kafka, Redis, PostgreSQL or Kubernetes. No web form, no tier-1 queue.
- T+15:00Live
Senior engineer on your bridge
A senior engineer who already knows your environment joins a live bridge, so nobody has to re-explain your topology.
- T+30:00If needed
Specialists across the stack join
When an incident crosses components, the engineers who know each one join. For RabbitMQ, the path runs to the core team.
- AfterEvery P1
Written root-cause analysis
Root cause, the fix applied and the prevention steps, delivered after every P1.
Pricing
What does AceMQ Databricks support cost?
AceMQ Databricks support contracts typically run $24,000–72,000 a year, depending on SLA level, scope (number of clusters or nodes, and environments) and deployment (self-managed, cloud-managed or Kubernetes).
| What moves the price | How it changes the contract |
|---|---|
| SLA level | Business-hours cover costs less than 24/7 cover with a 15-minute emergency response. |
| Scope | More clusters or nodes, and more environments, make a larger contract. |
| Deployment | Self-managed, cloud-managed and Kubernetes estates need different amounts of hands-on work. |
Covering several technologies under one contract, for example RabbitMQ, Kafka and Redis, earns an aggressive discount.
Support In Practice
Databricks problems we've already solved
Databricks support and remediation work from AceMQ. Each card links to the full write-up: the workspace, what AceMQ diagnosed or changed, and the result.
Databricks DBU Cost Governance
Global Payments Processor
Right-sizing Databricks compute by moving scheduled work off all-purpose clusters and tightening autoscaling, instance selection, and idle timeouts.
Databricks
Apache SparkDelta Lake
TerraformDatabricks Delta Small-File Remediation
Digital Retail Platform
Fixing Delta tables where streaming writes and over-partitioning have produced millions of tiny files, stalling reads and vacuum operations.
DatabricksDelta LakeApache SparkDatabricks Job Cluster Failure Support
Insurance Services Group
Named-engineer support for production Databricks job failures — driver OOM, spot reclamation, library conflicts, and workflow retry storms.
DatabricksApache SparkDelta LakeApache Spark Workload and Cost Assessment
Media Streaming Provider
Profiling a Spark estate to find over-provisioned jobs, redundant pipelines, and workloads better served by something other than Spark.
Apache SparkDatabricksDelta Lake
KubernetesDatabricks Unity Catalog Migration
Healthcare Analytics Provider
Migrating off the legacy Hive metastore to Unity Catalog with external location mapping, table upgrades, and a grant model that survives audit.
DatabricksDelta LakeApache SparkTerraform
Support options
Four tiers of Databricks support.
AceMQ offers Databricks support in four tiers, each handing more of the day-to-day work to AceMQ: 24/7 support and incident management for jobs and pipelines your team runs, proactive monitoring that finds cost and Delta table problems first, managed Databricks inside your own cloud account, and fully managed Databricks run on your behalf from an AceMQ-managed cloud account and infrastructure, with the pipeline tooling around it. All three clouds and the Databricks Runtime LTS releases are covered.
- Tier 1
Databricks 24/7 Support & Incident Management
You run it, we back you up
- 15-minute P1 response, 24/7, from an engineer who reads the Spark UI
- Triage across cluster, Delta table, Unity Catalog and cloud IAM faults
- The Databricks case opened with our diagnosis attached when the fault is theirs
Best for: Data engineering teams that run Databricks themselves and need senior escalation cover for production pipelines.
- Tier 2
Databricks 24/7 Support with Proactive Monitoring
You run it, we watch it with you
- Quarterly DBU spend and cluster policy reviews
- Delta small-file, OPTIMIZE and VACUUM checks on your busiest tables
- Alerts for the Databricks Runtime LTS you run, and a plan before it leaves support
Best for: Teams that want the idle all-purpose cluster or the small-file pile-up found in a review, not on the invoice.
- Tier 3
Managed Databricks on Your Infrastructure
We run it in your cloud or data centre
- AceMQ holds the pager for jobs, pipelines and workspace health
- Cluster policy, budget and tagging changes through reviewed deployments
- Runtime LTS upgrades rehearsed against your real jobs
Best for: Regulated estates that must keep data and compute inside their own cloud boundary.
- Tier 4
Fully Managed Databricks on AceMQ Infrastructure
We host it and run it, as a service
- Kafka and RabbitMQ sources that feed the lakehouse, run and monitored by AceMQ
- Orchestration and deployment tooling hosted on AceMQ infrastructure
- Pipeline lag, failures and DBU alerts routed to one on-call
Best for: Teams that want Databricks and the platform around it run for them, without building a pipeline operations team.
| Who does what | Tier 1 24/7 support | Tier 2 Proactive monitoring | Tier 3 Managed, your infrastructure | Tier 4 Fully managed by AceMQ |
|---|---|---|---|---|
| Who runs Databricks day to day? | Your team | Your team, with AceMQ reviews | AceMQ | AceMQ |
| Where does it run? | Your infrastructure | Your infrastructure | Your cloud account or data centre | AceMQ infrastructure |
| 24/7 incident response | 15-minute P1 response | 15-minute P1 response | 15-minute P1 response | 15-minute P1 response |
| Health checks and CVE alerts | On request | Scheduled | Included | Included |
| Patching and upgrades | Your team, AceMQ advises | Planned with AceMQ | Done by AceMQ | Done by AceMQ |
| Monitoring and on-call | Your team | Your team | AceMQ | AceMQ |
| Data, keys and accounts | Yours | Yours | Yours | Your data, hosted by AceMQ |
Every Tier can start as an ad hoc engagement or an annual partnership, and you can move up or down a Tier as your team changes.
One partner for Databricks
Everything else AceMQ does for Databricks.
Support is one part of what AceMQ does for Databricks. The same engineers deliver consulting, health checks, migrations and end-of-life cover, and support the systems that usually run next to it.
- ConsultingDatabricks consulting and services
- AssessmentDatabricks health check
- MigrationDatabricks migration
- LifecycleEnd-of-life software support
- Enterprise supportOpen source support across your whole stack
- GuideDatabricks guide
Support for the rest of your stack
Messaging & Streaming
- RabbitMQ supportMessaging & Streaming
- Apache Kafka supportMessaging & Streaming
- IBM MQ supportMessaging & Streaming
- ActiveMQ supportMessaging & Streaming
- Azure Service Bus supportMessaging & Streaming
- Amazon SQS supportMessaging & Streaming
- Google Cloud Pub/Sub supportMessaging & Streaming
- Redpanda supportMessaging & Streaming
- Apache Pulsar supportMessaging & Streaming
- Message Queuing supportMessaging & Streaming
Databases & Caching
Search & Analytics
Lakehouse & Compute
Observability & Monitoring
Data Integration
Cloud & Containers
API Management
See support for all 51 technologiesShow fewer
Related services
What does AceMQ Databricks support include?
Our Databricks support covers 24/7 emergency incident response with a 15-minute SLA, root cause analysis on every P1, performance and cluster-sizing tuning, DBU cost governance, CVE and patch advisory, and migration or Unity Catalog support. Every ticket is handled by a named senior engineer assigned to your account, with no tier-1 triage layer.
Can you help us get our Databricks costs under control?
Yes — cost governance is one of the most common reasons enterprises bring us in. We audit cluster policies, job-vs-all-purpose cluster usage, autoscaling configuration, and auto-termination settings, then run quarterly reviews so idle clusters and cold-start waste get caught before the next invoice, not after.
Do you support Databricks across AWS, Azure, and GCP?
Yes. We support Databricks on all three cloud providers, including PrivateLink and VNet/VPC peering deployments. The core engine issues — cluster sizing, Delta table layout, skewed joins — are consistent across clouds; the cloud-specific issues are IAM, networking, and billing integration, which we also cover.
Can you help migrate from a Hive metastore to Unity Catalog?
Yes. We plan and execute Hive metastore to Unity Catalog migrations, including external location grant mapping, lineage remediation for tables that weren't cleanly registered, and a validation pass to confirm nothing was silently dropped during the move.
How fast do you respond to a Databricks production outage?
P1 emergencies — pipelines down, cluster-wide job failures, a workspace that won't provision — get a 15-minute first response with a senior engineer joining a live bridge. P2 critical issues are 1 hour, P3 high-priority 4 hours, and P4 standard questions next business day. These are contractual targets.
Do you support Databricks alongside Snowflake or Starburst?
Yes, and this is where a lot of value shows up. Many enterprises run Databricks, Snowflake, and Starburst together as a lakehouse-plus-warehouse stack. We support all three, which means we can diagnose issues that live at the boundary between them instead of pointing at 'the other vendor.'
How does this compare with Databricks' own support plans?
Databricks sells Business, Production and Mission Critical support plans. On Production and Mission Critical, a Severity 1 case gets a first response within 1 hour, and a Mission Critical severity issue within 15 minutes with an escalation manager assigned. Those plans fix the platform. They do not tune your jobs, Delta tables or cluster policies, which is the layer AceMQ Databricks support covers, and we write the Databricks case with the diagnosis attached when the fault is theirs.
Can you move our jobs off a Databricks Runtime LTS before it reaches end of support?
Yes. Each Databricks Runtime LTS release gets three years of support, so 14.3 LTS ends on 1 February 2027 and 15.4 LTS on 19 August 2027, and jobs pinned to those runtimes have to move. We inventory which clusters and jobs run each runtime from the system tables, test them against the target LTS, including Spark and library changes, and stage the rollout so a failing job rolls back instead of blocking a pipeline.
Can we work with AceMQ on Databricks without a long contract?
Yes. AceMQ works on Databricks 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 Databricks support or consulting cost?
Every AceMQ proposal is tailored to your requirements: your Databricks 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 Databricks is deployed, with aggressive discounts when several technologies share one contract. Request a quote and AceMQ replies within 1 hour.
Let's talk
A Runaway Cluster Shouldn't Be Your First Warning Sign
Reactive cover, proactive reviews, or AceMQ running Databricks for you on your infrastructure or ours. A senior engineer scopes it with you, and quote requests are answered within 1 hour.
A senior Databricks engineer, not a sales queueQuote requests answered within 1 hourAd hoc engagements or an annual partnership
Production incident right now?Email a Databricks Architect
Get in Touch
Talk to a Databricks Expert
Send us a message and we'll follow up within 1 hour — or book a free 30-min consultation directly.
305-204-2607info@acemq.com66 W. Flagler St. 9th Floor
Miami, FL 33130
Miami, FL 33130
Prefer to talk now? Call us directly or use the consultation tab to find a time that works.