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 is AceMQ Databricks support priced?

Support is priced as an annual contract based on your workspace size, required SLA tier, and coverage hours — not per incident or per ticket. Calling us during an outage never carries a cost decision. Contact us for a quote; we turn them around within 24 hours.

Databricks Support

24/7 Databricks Support with a 15-Minute Emergency SLA

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.

Senior Databricks engineers on call right now — 24/7/365

15 min emergency SLA24 /7 global coverage130 + enterprise customers26 + countries served

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

Escalation Path

Your first hour of a Databricks 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 Databricks 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

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 seeingWhat we check firstTypical resolution

Job clusters spend most of their billed time on cold start

What we check firstAutoscaling 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.

Typical resolution1–2 hours

Delta table writes slowing down, thousands of tiny files piling up

What we check firstAverage file size via DESCRIBE DETAIL and OPTIMIZE history. High-frequency streaming writes without auto-compaction or a scheduled OPTIMIZE job are the standard cause.

Typical resolution1–3 hours

Driver returns 'not responsive' or crashes with OOM

What we check firstNotebook and job code for collect() or toPandas() calls pulling a full dataset to the driver node, plus broadcast join thresholds against actual table size.

Typical resolutionUnder 2 hours

One executor runs 10x longer than every other task in the stage

What we check firstSpark 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.

Typical resolution1–3 hours

Unity Catalog permission errors after a Hive metastore migration

What we check firstExternal location grants, metastore assignment, and which tables were never registered into Unity Catalog — lineage gaps trace back to this almost every time.

Typical resolution2–4 hours

DBU spend blows past budget with no workload change

What we check firstCluster 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.

Typical resolutionSame day

VACUUM removes files a running time-travel query still needs

What we check firstRetention interval configuration against in-flight time-travel queries and any concurrent OPTIMIZE jobs touching the same table.

Typical resolutionUnder 2 hours

Photon isn't engaging on a workload that should qualify

What we check firstCluster 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.

Typical resolution1–2 hours

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

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Support In Practice

Databricks problems we've already solved

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

All use cases

Assessment

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.

DatabricksApache SparkDelta Lake

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Remediation

Databricks 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 Spark

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Support

Databricks 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 Lake

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Assessment

Apache 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

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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.

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.

Root Cause Analysis

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

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.

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.

CVE & Patch Advisory

Proactive alerts for security issues affecting your exact Databricks Runtime version, with tested upgrade paths that account for job compatibility.

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.

Anywhere You Run It

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 AWSDatabricks on AzureDatabricks on GCPUnity CatalogDelta LakeDatabricks SQL WarehousesJob clusters & all-purpose clustersMLflowVPC / VNet peering & PrivateLinkHybrid cloudAir-gapped / restricted network

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.

FAQ

Databricks support questions

A Runaway Cluster Shouldn't Be Your First Warning Sign

Whether you need emergency response tonight or a support contract that catches cost and performance problems before they hit production, AceMQ staffs every engagement with a named senior Databricks 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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