Databricks Consulting & Support

Databricks Consulting & Services for Enterprises

AceMQ engineers design, tune, and operate Databricks lakehouses for financial services, healthcare, and manufacturing teams. Every engagement is staffed by a named senior engineer — no ticket queues, no junior triage.

A senior engineer, not an SDR. No sales sequence attached.

Senior SMEs
11+
Emergency SLA
<15min
Customers
160+
Countries Served
26+

Without re-platforming the lakehouse or rewriting every notebookFor production incident cover, see AceMQ Databricks 24/7 support.

Customers

  • Microsoft
  • Henry Schein
  • Enbridge
  • Gojek

Partners

  • Broadcom
  • Redis
  • VMware Tanzu
  • OSSeva

The Problem

Why teams call us about Databricks

Eight Databricks problems that teams bring to AceMQ, from idle all-purpose clusters and Delta small files to a partial Unity Catalog rollout and DBU spend nobody can attribute. If one describes your last bill or your last slow run, that is where the review starts.

  1. 01

    All-purpose clusters stay running long after the last job finished

  2. 02

    Small files in Delta tables make every read scan far more files than the row count justifies

  3. 03

    Job clusters are sized for the worst-case run and pay for it on every ordinary one

  4. 04

    Photon was switched on everywhere without checking which workloads actually benefit

  5. 05

    Production notebooks have no version control and no repeatable deployment path

  6. 06

    Unity Catalog was adopted partially, so permissions live in two places and agree in neither

  7. 07

    A shuffle-heavy job spills to disk and the run time doubles without the data doubling

  8. 08

    DBU consumption grows quarter on quarter and cannot be attributed to a team or a workload

A good fit if you run Databricks in production on AWS, Azure or GCP and want senior engineers who read the Spark UI, the cluster event log and the DBU bill, whether you are optimizing, migrating onto it or running it day to day.

Not a fit for small teams with non-critical workloads. AceMQ is not a budget consultancy: we work with enterprises running business-critical and compliance-heavy Databricks.

Running Databricks in production? Get a quote from a senior engineer, answered within one hour.

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How it breaks, and the fix

What happens when one skewed task holds up a Databricks stage?

In a Spark stage on Databricks, a join or aggregation on a skewed key sends most of the rows to a single task. That task runs long after the others finish, the cluster sits mostly idle on a bill that keeps running, and every downstream job waits for the stage.

One skewed task, one slow stage

A Delta source, three tasks, one shuffle, one output table

Illustrative

  1. The three tasks get similar slices of the data and finish together, so the shuffle and the gold table complete on schedule.

  2. A handful of customer keys hold most of the rows. Task 2 gets all of them, spills to disk and runs for an hour while tasks 1 and 3 finish in minutes. The job cluster stays up billing DBUs, and the gold table is late.

  3. Adaptive query execution splits the skewed partition, the small side of the join is broadcast and the hot keys are handled on their own. The three tasks finish close together and the job cluster shuts down sooner.

A stage held up by one task is one of the most common reasons teams call AceMQ about Databricks. The Spark UI shows it: one task far past the median on duration and shuffle read size. The fix is adaptive query execution with skew join handling, a broadcast of the small side or a salted key, a cluster sized for the stage that is not skewed, and OPTIMIZE or liquid clustering so the table layout stops feeding the skew. AceMQ also checks how long the cluster stayed up waiting for that one task.

Our Services

Databricks Consulting & Services

AceMQ runs five Databricks service lines: lakehouse architecture, cost and performance optimization, migration to Databricks, managed operations and a lakehouse health check. A senior engineer leads each one, and any of them can roll into 24/7 support with a 15-minute emergency SLA.

  • 01

    Lakehouse Architecture & Implementation

    We design medallion-architecture lakehouses on Delta Lake that balance query performance, storage cost, and governance from day one.

    • Bronze/silver/gold medallion layer design with Delta Lake table optimization (Z-ordering, OPTIMIZE, VACUUM scheduling)
    • Unity Catalog metastore design: catalogs, schemas, external locations, and credential vending
    • Migration from legacy Hive metastore to Unity Catalog with zero-downtime cutover planning
    • Lakeflow Declarative Pipelines (formerly Delta Live Tables) architecture for declarative ETL with data quality expectations
  • 02

    Cost & Performance Optimization

    DBU spend grows unpredictably when cluster policies aren't governed. We tune workloads and enforce spend controls that cut compute cost without slowing delivery.

    • Job cluster vs. all-purpose cluster strategy audits to eliminate idle DBU burn
    • Cluster right-sizing, autoscaling policy tuning, and instance pool configuration
    • Photon engine adoption analysis and query plan tuning for slow Spark SQL jobs
    • DBU spend governance: cluster policies, budgets, and chargeback tagging by team
  • 03

    Migration to Databricks

    Moving off a traditional warehouse or an aging Hadoop cluster is the most common Databricks engagement we run. We plan the cutover so reporting never breaks.

    • Traditional warehouse (Teradata, Netezza, on-prem SQL Server) to Databricks SQL migration
    • Hadoop/Hive to Delta Lake migration with schema and partition strategy redesign
    • ETL pipeline conversion from legacy tools (Informatica, SSIS, Talend) to Databricks workflows
    • Parallel-run validation and row-level reconciliation before legacy decommission
  • 04

    Managed Databricks Operations

    Once you're live, someone has to own job failures, workspace sprawl, and cost creep. We provide ongoing operational coverage so your data team can focus on building.

    • 24/7 job failure triage and incident response for production pipelines
    • Workspace and CI/CD governance using Declarative Automation Bundles (formerly Databricks Asset Bundles) for repeatable deploys
    • MLOps pipeline support: MLflow experiment tracking, model registry, and serving endpoint monitoring
    • Ongoing cluster policy and access control maintenance as teams and workloads grow
  • 05

    Lakehouse Health Check & Assessment

    A structured review of your Databricks environment — architecture, cost, governance, and security — with a prioritized remediation plan.

    • DBU spend and cluster utilization audit against workload patterns
    • Unity Catalog governance and access control review
    • Delta table health check: file compaction, small-file problems, and stale statistics
    • Security posture review: network configuration, secret scopes, and credential passthrough
  • 15 MIN SLA

    24/7 Databricks Support

    Named senior engineers on your account — 15-minute emergency response, no ticket routing, no junior triage.

    • 15-minute emergency response SLA
    • Named engineer, zero cold-start
    • Proactive CVE & health monitoring
    • Quarterly deployment reviews
    Compare the four Databricks support tiers

Want AceMQ to take this off your team's plate? Tell us about your Databricks estate.

Contact Us

24/7 Support

Databricks Support When It Matters Most

AceMQ Databricks support gives you named senior engineers and a 15-minute first response when production is down, 24/7. Lower severities get 1-hour, 4-hour and next-day responses. The same tiers apply on AWS, Azure and GCP, alongside Databricks' own support plans.

  1. 15 minfirst responseEmergencyProduction down, messages not flowing, queue manager failure
  2. 1 hourfirst responseCriticalSevere degradation, high error rates, approaching capacity
  3. 4 hoursfirst responseHighPerformance issues, configuration problems, non-critical failures
  4. 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

SLA 15:00
T+Elapsed15:00
Engineer on your bridge

15-minute P1 response, 24/7.
A response time, not a resolution time.

  1. T+00:00

    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.

    Paged
  2. T+15:00

    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.

    Live
  3. T+30:00

    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.

    If needed
  4. After

    Written root-cause analysis

    Root cause, the fix applied and the prevention steps, delivered after every P1.

    Every P1

How Our Support Actually Works

Beyond SLAs — the model behind senior-only, zero-cold-start expert access.

  • Named Engineers on Your Account

    Every ticket is handled by a senior SME assigned to your account — not a pool of anonymous agents. Zero cold-start. No re-explaining your environment.

  • Live Escalation on Any Ticket

    Any ticket can be escalated to a live session with your named engineer via calendar booking. No gatekeeping, no approval required — direct access, always.

  • Proactive Risk Mitigation

    Quarterly health checks on your deployment plus shared intelligence from 50+ support customers — we surface risks before they reach production.

  • Critical Bug & CVE Intelligence

    Proactive alerts on critical bugs and CVEs affecting your exact version, with version compliance monitoring so you're never caught off guard.

Compare Databricks support plans

How It Works

What actually happens next

A Databricks engagement with AceMQ runs in four stages: a workspace and workload review of 1 to 2 weeks, written findings within a week, changes made with your engineers workload by workload, then a handover with runbooks. Each stage ends in writing, so your team sees what changes and why first.

1–2 weeks

Workspace and workload review

We look at cluster configuration and autoscaling behaviour, job run history and spill, Delta table layout and file sizes, how far Unity Catalog coverage actually extends, and where the DBUs are being spent.

Within a week

Written findings and plan

Which jobs and which table layouts drive the cost and the run time, what is merely correlated with it, and what to change first. Written so your own engineering leadership can sign it off.

Scoped per engagement

Implementation alongside your team

Cluster policies, table optimisation and job changes rolled out workload by workload, each validated against the previous run times before the old configuration is retired.

Ongoing

Handover and optional cover

Runbooks for cluster policy changes, table maintenance and cost attribution, plus 24/7 support with a 15-minute emergency SLA if you want it.

  • Written findings before any change
  • Every change staged per workload and reversible
  • Your engineers in the room
  • You keep the runbooks
  • Ad hoc engagements or an annual partnership

A senior engineer, not an SDR. No sales sequence attached.

See the Databricks health check

Customer Success

Real Databricks Results

Databricks and Spark work from AceMQ: DBU cost governance, a Unity Catalog migration, Delta small-file remediation and job cluster support. Each card links to the full write-up: the environment, what AceMQ assessed or changed, and the result.

1 / 5 case studies

  • 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 LakeTerraform
    Read case study : Databricks DBU Cost Governance
  • Consulting

    Databricks 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
    Read case study : Databricks Unity Catalog Migration
  • 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
    Read case study : Databricks Delta Small-File Remediation
  • 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
    Read case study : Databricks Job Cluster Failure Support
  • 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 LakeKubernetes
    Read case study : Apache Spark Workload and Cost Assessment
All use cases

Solutions

Every way AceMQ works with Databricks.

AceMQ covers Databricks end to end: lakehouse consulting and migrations, 24/7 support and incident management, proactive monitoring, managed operations in your cloud account or from an AceMQ-managed account, runtime upgrade planning, and a fixed-price health check.

Projects

Build, fix or move Databricks

Fixed-scope work led by a senior engineer: architecture and new builds, a health check of what you run today, or a migration with a reversible cutover.

Support and managed services

Keep Databricks running

Four tiers of ongoing cover once you are live. Each tier hands more of the day-to-day work to AceMQ, from a 15-minute emergency SLA to AceMQ hosting and running Databricks for you.

Compare the four Databricks support tiers

Side by side

Databricks classic or serverless compute: what do you still own?

On Databricks classic compute, clusters run in your own cloud account and you choose instance types, runtime version and policies. Serverless compute runs in a compute plane Databricks manages and removes most of that tuning. On either, job design, table layout, Unity Catalog permissions and DBU attribution stay with your team, and that is the layer AceMQ covers.

Databricks classic compute compared with serverless compute on who sizes the compute, how much you can tune, runtime upgrades, cost shape, networking and who answers an incident
QuestionClassic compute (your cloud account)Serverless compute (Databricks-managed)
Who provisions and sizes the compute?Your team, through cluster policies, instance pools and autoscaling limits.Databricks provisions and scales it. You choose the workload and the budget policies, not the instance type.
How much can you tune?Spark configuration, init scripts, libraries, instance families and the Databricks Runtime version.Far fewer settings. The runtime and most Spark configuration are managed for you, so a job tuned through low-level settings may need rework.
Who plans runtime upgrades?You do. 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.Databricks manages the runtime, so there is no LTS date to plan around. Jobs still need testing when the environment version changes.
What does the cost look like?DBUs plus the cloud instances in your own account. Idle all-purpose clusters and oversized job clusters are the usual waste.A DBU charge that includes the compute, with no idle cluster to forget. A badly written job gets expensive faster, because only a budget policy caps it.
How do networking and data access work?Compute runs in your VPC or VNet, so peering, PrivateLink, firewalls and egress rules are yours to configure.Compute runs in Databricks' compute plane and reaches your storage over connectivity you configure. Network and access policies need review for regulated data.
Who answers an incident?Databricks support handles the platform. Cluster policies, job design and cloud IAM stay with your team, or with AceMQ: a named senior engineer, 24/7, a 15-minute emergency response and a written root-cause analysis for every P1.Databricks support handles the platform. Job logic, table layout, permissions and spend stay with your team, or with AceMQ: a named senior engineer, 24/7, a 15-minute emergency response and a written root-cause analysis for every P1.

To compare Databricks with a warehouse platform, read Databricks vs Snowflake compared. If you are still on Hadoop, the Hadoop to Databricks migration guide covers the move step by step.

Not sure which option fits? A senior Databricks engineer will scope it with you.

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One partner, not five

We Support Your Entire Tech Stack

Databricks rarely fails in isolation. AceMQ covers the full surrounding infrastructure — so one team owns the whole path instead of pointing at each other.

Messaging & Streaming

Databases & Caching

Search & Analytics

Lakehouse & Compute

Observability & Monitoring

Data Integration

Cloud & Containers

API Management

VMware Tanzu

See all 51 technologies we supportShow fewer

Guides, articles and comparisons

Go deeper on Databricks.

Guides from AceMQ engineers on moving off Hadoop to Databricks and on how Databricks compares with Snowflake, plus the AceMQ services for the platforms that usually run next to it.

Related services

Ready to talk Databricks? Get a quote or send us a message.

Contact Us

FAQs

Databricks Questions Answered

Answers on Databricks from AceMQ: Databricks Runtime end of support, what Databricks support leaves to you, who answers when a job fails, how an engagement starts, working with your team, slow and costly jobs, managed operations, and migrations onto Databricks.

Still have questions about Databricks? Email an Expert

Which Databricks Runtime versions are supported, and when do they reach end of support?

Each Databricks Runtime LTS release is supported for three years from its release date. 14.3 LTS ends on 1 February 2027, 15.4 LTS on 19 August 2027, 16.4 LTS on 9 May 2028, 17.3 LTS on 22 October 2028 and 18 LTS on 10 June 2029. AceMQ supports Databricks across these runtimes and plans the moves between them.

Does AceMQ replace Databricks support?

No. Databricks support fixes the platform, and AceMQ covers the layer above it: job design, cluster policies, Delta table layout, Unity Catalog permissions and DBU spend. When the fault is inside Databricks, AceMQ opens the case with the diagnosis attached. Many teams hold both, because the two cover different failures.

Who answers when a Databricks job fails in production, and how fast?

A named senior AceMQ engineer joins a live bridge within 15 minutes for a P1, around the clock. They start with the job run logs and the cluster event log to separate a code, data or infrastructure fault, then stay until the pipeline is stable. A written root-cause analysis follows every P1.

How does a Databricks engagement with AceMQ start?

It starts with a scoping call, booked in our reply within 1 hour of your request, then a workspace and workload review of 1 to 2 weeks. Written findings follow within a week: what drives your cost or run time, and what to change first. Nothing in your workspace changes before you have read them.

How does AceMQ work with our in-house data engineering team?

AceMQ engineers work alongside your team, not in place of it. Changes roll out workload by workload with your engineers reviewing each one, validated against previous run times, and the engagement ends with runbooks for cluster policy changes, table maintenance and cost attribution. Your team keeps ownership of the lakehouse.

Should we run Databricks ourselves or have AceMQ manage it?

Run it yourself if your data engineers have time to hold the pager and watch cost. AceMQ managed operations suit teams that do not: AceMQ operates your workspaces inside your own cloud account, covering jobs, cluster policies, Unity Catalog and spend, with 24/7 on-call. Where AceMQ operates in your account, your data and credentials stay in it; AceMQ can also run it from an AceMQ-managed account on your behalf.

Can AceMQ help move from Hadoop or Snowflake to Databricks?

Yes. AceMQ plans migrations from Hadoop, Cloudera, Snowflake and BigQuery onto Databricks, and from the Hive metastore to Unity Catalog. Every workload runs in parallel against the old platform and is reconciled row by row before cutover, so reporting does not break. The work starts with a written assessment.

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

Ready to Stabilize Your Databricks Environment?

Whether you need emergency support, a lakehouse health check, a migration partner, or ongoing managed operations — AceMQ staffs every engagement with a named senior Databricks engineer. Quote requests answered within 1 hour.

A senior Databricks engineer, not a sales queueAbout your workspaces, not a pitch deckQuote requests answered within 1 hour

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.

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

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