Apache Hadoop Consulting & Support

Apache Hadoop Consulting & Support for Enterprises

Many enterprises are still running Hadoop in production. AceMQ engineers keep those clusters stable and secure, and for teams ready to move, plan the migration to a modern lakehouse. We staff every engagement with a named senior engineer and tell you honestly whether to maintain or modernize.

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

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

Without a big-bang migration off the clusterFor production incident cover, see AceMQ Apache Hadoop 24/7 support.

Customers

  • Microsoft
  • Henry Schein
  • Enbridge
  • Gojek

Partners

  • Broadcom
  • Redis
  • VMware Tanzu
  • OSSeva

The Problem

Why teams call us about Apache Hadoop

Seven Apache Hadoop problems that teams bring to AceMQ, from a NameNode heap full of small-file metadata and YARN queues nobody has reviewed since the cluster was built, to hand-managed Kerberos keytabs and a distribution that no longer gets vendor patches. If one describes your cluster, the review starts there.

  1. 01

    The NameNode heap is full of small file metadata and restarts take longer every quarter

  2. 02

    YARN queues were configured years ago and no longer reflect who needs the capacity

  3. 03

    Kerberos keytabs are managed by hand and tickets expire at inconvenient moments

  4. 04

    Vendor support for the distribution has ended and the cluster runs an unsupported version

  5. 05

    Nobody left in the organisation knows how the Oozie scheduling actually fits together

  6. 06

    Data nodes are failing and replacement hardware for that generation is hard to source

  7. 07

    HDFS is at capacity and nobody can say which datasets are still read

  8. 08

    The renewal quote for commercial Hadoop support now exceeds the value of the workloads it protects

A good fit if you run Hadoop in production, often Kerberized and in a regulated environment, and want senior engineers who still know HDFS and YARN internals, whether you plan to keep the cluster running or to move off it.

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 Apache Hadoop.

Running Apache Hadoop 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 reducer holds up a Hive job on YARN?

In a Hive or MapReduce job on YARN, a skewed key sends most of the rows to a single reducer. The other reducers finish early, but the job cannot commit until that one is done, so the output is late and every job waiting on it queues behind it.

One skewed reducer, one late job

HDFS input, three reducers, one job commit, one output table

Illustrative

  1. The three reducers get similar amounts of data and finish together, so the job commits on time and its queue capacity is free for the next job.

  2. A few keys hold most of the rows. Reducer 2 gets them all and runs for hours while reducers 1 and 3 finish early. The job cannot commit, the output table is late and the jobs that wait on it queue behind it.

  3. Skew handling is switched on, the hot keys are processed separately, and the reducer count is set from the data size. The reducers finish close together and the job commits inside its window.

A skewed reducer is one of the most common reasons teams call AceMQ about Hadoop batch jobs. The task counters and the YARN application view show it: one task far above the rest on duration and input bytes. The fix is Hive skew settings such as hive.optimize.skewjoin and hive.groupby.skewindata, a salted key or more reducers sized from the data, or Spark with adaptive query execution if the job is moving anyway. AceMQ also checks the YARN queue limits, because one stuck job should not starve everyone else.

Our Services

Apache Hadoop Consulting & Services

AceMQ runs five Hadoop service lines: cluster hardening and capacity planning, migration off Hadoop to a lakehouse, security hardening for regulated environments, managed operations and a Hadoop health check. A senior engineer leads each one, and AceMQ tells you plainly whether to maintain or modernize.

  • 01

    Cluster Hardening & Capacity Planning

    For enterprises still running Hadoop in production, we keep the cluster stable, secure, and sized correctly for current load — without pretending it isn't aging infrastructure.

    • HDFS NameNode capacity planning and federation design for large metadata volumes
    • YARN resource pool and queue configuration for multi-tenant job scheduling
    • Rack awareness and replication factor tuning for storage efficiency and fault tolerance
    • DataNode disk failure monitoring and decommissioning runbooks
  • 02

    Migration Off Hadoop to a Modern Lakehouse

    This is the conversation most Hadoop prospects actually want to have. We plan and execute migrations from HDFS/Hive to Databricks, Spark on cloud object storage, or another modern platform — with a realistic timeline, not a marketing one.

    • Workload inventory and prioritization: which jobs move first, which get retired
    • HDFS to cloud object storage (S3, ADLS, GCS) data migration with checksums and validation
    • Hive metastore to Unity Catalog or Glue Catalog migration and schema conversion
    • Parallel-run validation and legacy cluster decommission planning
  • 03

    Security Hardening for Regulated Environments

    Enterprises in financial services and healthcare that are staying on Hadoop for now need Kerberos and encryption done right — not a checkbox implementation that breaks job scheduling.

    • Kerberos authentication setup and principal/keytab management across the cluster
    • Apache Ranger or Sentry authorization policy design for fine-grained access control
    • Encryption at rest (HDFS transparent encryption) and in transit (TLS) configuration
    • Audit logging and compliance reporting for regulated data access
  • 04

    Managed Hadoop Operations

    Ongoing operational coverage for clusters running end-of-life or aging distributions — someone has to keep YARN scheduling and HDFS replication healthy while you plan the exit.

    • 24/7 monitoring for NameNode health, under-replicated blocks, and YARN queue starvation
    • Upgrade planning off end-of-life Cloudera/Hortonworks (CDH/HDP) distributions
    • Capacity and cost assessment for aging on-prem hardware nearing refresh cycles
    • Disaster recovery architecture: HDFS snapshot policy and cross-cluster replication with DistCp
  • 05

    Hadoop Health Check & Assessment

    A structured assessment of your Hadoop cluster's health, security posture, and migration readiness, with a clear recommendation on maintain vs. modernize.

    • Cluster utilization and capacity review against current and projected workload
    • Security posture audit: Kerberos configuration, authorization policy, and encryption coverage
    • Distribution end-of-life and support status review (Cloudera, Hortonworks, legacy MapR)
    • Migration readiness assessment scoring workloads by complexity and modernization priority
  • 15 MIN SLA

    24/7 Apache Hadoop 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 Apache Hadoop support tiers

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

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24/7 Support

Apache Hadoop Support When It Matters Most

AceMQ Apache Hadoop support gives you named senior engineers and a 15-minute first response when the NameNode or YARN fails, 24/7. Lower severities get 1-hour, 4-hour and next-day responses. The same tiers apply to Apache Hadoop and the Cloudera and Hortonworks distributions.

  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 Apache Hadoop support plans

How It Works

What actually happens next

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

1–2 weeks

Cluster and inventory review

We look at NameNode and HDFS health, small file volume, YARN queue usage against real demand, Kerberos and security posture, and which datasets and jobs are still genuinely in use.

Within a week

Written findings and plan

What must be fixed to keep the cluster safe to run, what is worth migrating, and what can simply be retired. An honest maintain-or-modernise recommendation your leadership can act on.

Scoped per engagement

Implementation alongside your team

Stabilisation first — the things that would otherwise cause an incident — then migration in tranches by dataset and workload, with the old cluster serving until each tranche is verified.

Ongoing

Handover and optional cover

Runbooks for NameNode recovery, node replacement and Kerberos renewal, plus 24/7 support with a 15-minute emergency SLA if you want it.

  • Written findings before any change
  • Every change staged and reversible, with the cluster kept running
  • 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.

Customer Success

Real Apache Hadoop Results

Hadoop work from AceMQ: a NameNode heap remediation, YARN scheduler support, a cluster exit assessment and a Hadoop to lakehouse migration. Each card links to the full write-up: the cluster, what AceMQ assessed or changed, and the result.

1 / 6 case studies

  • Consulting

    Apache Hadoop to Lakehouse Migration

    National Insurance Carrier

    Moving off an aging Hadoop cluster to object storage and open table formats, with Hive, MapReduce, and Oozie workloads translated rather than lifted.

    Apache HadoopApache SparkDelta LakeApache Airflow
    Read case study : Apache Hadoop to Lakehouse Migration
  • Assessment

    Apache Hadoop Cluster Exit Assessment

    Regional Utility Operator

    Establishing what is actually running on a Hadoop cluster, what it costs to keep, and what a defensible migration sequence and timeline look like.

    Apache HadoopApache HiveApache SparkYARN
    Read case study : Apache Hadoop Cluster Exit Assessment
  • Remediation

    Apache Hadoop NameNode Heap Remediation

    Global Logistics Operator

    Relieving NameNode heap pressure and long GC pauses caused by small-file sprawl across HDFS, before the cluster loses its metadata service.

    Apache HadoopApache HiveApache SparkYARN
    Read case study : Apache Hadoop NameNode Heap Remediation
  • Support

    Apache Hadoop YARN Scheduler Support

    Public Sector Agency

    Named-engineer support for YARN queue starvation, container allocation failures, and NodeManager instability on production Hadoop clusters.

    Apache HadoopYARNApache SparkApache Hive
    Read case study : Apache Hadoop YARN Scheduler Support
  • Consulting

    Apache Spark Shuffle and Spill Tuning

    National Retail Bank

    Reducing shuffle write volume and disk spill on nightly batch jobs so the processing window fits inside the reporting deadline.

    Apache SparkApache HadoopYARNApache Hive
    Read case study : Apache Spark Shuffle and Spill Tuning
All use cases

Solutions

Every way AceMQ works with Apache Hadoop.

AceMQ covers Hadoop end to end: cluster consulting and migrations, 24/7 support and incident management, proactive monitoring, managed clusters on your infrastructure or on AceMQ's, cover for end-of-life distributions, and a fixed-price health check.

Projects

Build, fix or move Apache Hadoop

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 Apache Hadoop 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 Apache Hadoop for you.

Compare the four Apache Hadoop support tiers

Side by side

Keep Hadoop running or move to a lakehouse: which fits your cluster?

Hadoop 3.4 and 3.3 are still active Apache lines, so a healthy cluster can stay if it is hardened, patched and staffed. Moving to a lakehouse removes the hardware, the HDFS operations and the scarce skills, but it is a real migration. AceMQ supports both paths and says which fits, from your workload mix rather than a preference.

Keeping and hardening Hadoop compared with migrating to a lakehouse on main risk, security patches, the work involved, how it proceeds, whether it can be reversed and who answers an incident
QuestionKeep and harden HadoopMigrate to a lakehouse
What is the main risk?Falling behind on patches and losing the people who know HDFS and YARN internals.A migration that breaks reporting, because Hive tables, Oozie workflows and Ranger or Sentry policies were never inventoried.
What happens to security patches?Apache keeps 3.4 and 3.3 active. A distribution past vendor support gets none, so AceMQ supplies workarounds and backported mitigations.The platform vendor patches the service. You patch your own jobs and libraries.
What does the work involve?NameNode heap and small-file cleanup, YARN queue review, Kerberos renewal, disaster recovery for HDFS and a capacity plan for hardware that is hard to source.An inventory, the move from HDFS to object storage, Hive to Delta Lake tables, job conversion, and a parallel run until results match.
How does it proceed?In staged changes while the cluster keeps running.Phased by workload, with each phase validated in parallel before the old cluster is decommissioned.
Can you reverse the decision?Yes. Hardening does not block a later migration.Until cutover, yes, because the old cluster keeps running in parallel.
Who answers an incident?Your team, or AceMQ: a named senior engineer, 24/7, a 15-minute emergency response and a written root-cause analysis for every P1.The new platform's vendor for the platform, and your team or AceMQ for jobs and tables, with the same 15-minute response.

For the move to Databricks step by step, read the Hadoop to Databricks migration guide.

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

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One partner, not five

We Support Your Entire Tech Stack

Apache Hadoop 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 Apache Hadoop.

A guide from AceMQ engineers to moving from Hadoop to Databricks step by step, plus the AceMQ services for the Spark, Databricks and streaming platforms that usually replace or sit beside a Hadoop cluster.

Related services

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

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FAQs

Apache Hadoop Questions Answered

Answers on Apache Hadoop from AceMQ: supported versions, whether AceMQ supports Hadoop or only migration off it, NameNode heap exhaustion, end-of-life distributions, who answers when the cluster fails, how an engagement starts, migrating to a lakehouse, and managed operations.

Still have questions about Apache Hadoop? Email an Expert

Which Apache Hadoop versions are still supported?

Apache's download page lists 3.5.0 from April 2026, 3.4.x and 3.3.6, and the project's release-lines page names 3.4, 3.3 and 2.10 as active. The project publishes no fixed support period: a line stays active until the community declares it end of life. AceMQ supports Apache Hadoop and the Cloudera and Hortonworks distributions built on it, covering the current release and the five end-of-life release lines before it.

Do you still support Hadoop, or only migration off it?

Both. AceMQ keeps HDFS and YARN clusters stable and secure in production, and plans migrations to a lakehouse for teams that are ready. Whether to maintain or modernize is decided from your workload mix and patch position, and we say plainly if a fixable cluster does not justify a migration.

What causes NameNode heap exhaustion?

Almost always small-file sprawl. Every file and block takes roughly 150 bytes of NameNode heap regardless of its size, so millions of small files exhaust the heap even when total data looks modest. AceMQ finds the directories responsible, compacts or archives them, and sets limits so the pattern does not return.

Can you support a cluster on an end-of-life Cloudera or Hortonworks distribution?

Yes. AceMQ supports clusters on distributions that no longer receive vendor patches, with workarounds and backported mitigations for the issues that matter, and a read on exposure for regulated environments that cannot upgrade on the vendor's timeline. We also say whether migrating now or later makes more sense.

Who answers when the NameNode or YARN fails, and how fast?

A named senior AceMQ engineer joins a live bridge within 15 minutes for a P1, such as a NameNode down, a re-replication storm or frozen YARN queues, around the clock. A written root-cause analysis follows every P1. P2 is 1 hour, P3 is 4 hours and P4 is the next business day.

How does a Hadoop engagement start, and how do you work with our team?

It starts with a scoping call, booked in our reply within 1 hour of your request, then a cluster and inventory review of 1 to 2 weeks and written findings within a week. Changes are made with your engineers in staged steps, and the engagement ends with runbooks. Your team keeps ownership of the cluster.

What does migrating off Hadoop to a lakehouse involve?

An inventory of what is actually used, then moving data from HDFS to object storage, converting Hive tables to Delta Lake, converting jobs and mapping Ranger or Sentry permissions. Every workload runs in parallel against the old cluster until results reconcile. AceMQ starts with an honest assessment, not a pitch.

Should we run Hadoop ourselves or have AceMQ manage it?

Run it yourself if you still have engineers who know HDFS and YARN internals and can hold the pager. AceMQ managed operations suit teams that do not: AceMQ operates your clusters on-premises, in your cloud account, in an air-gapped site or on AceMQ infrastructure, covering patching, capacity, Kerberos upkeep and on-call, while a migration is planned.

Can we work with AceMQ on Apache Hadoop without a long contract?

Yes. AceMQ works on Apache Hadoop 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 Hadoop support or consulting cost?

Every AceMQ proposal is tailored to your requirements: your Apache Hadoop 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 Hadoop 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 or Modernize Your Hadoop Environment?

Whether you need emergency support, a security hardening engagement, a migration partner, or ongoing managed operations while you plan your exit — AceMQ staffs every engagement with a named senior Hadoop engineer. Quote requests answered within 1 hour.

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

Production incident right now?Email an Apache Hadoop Architect

Get in Touch

Talk to an Apache Hadoop Expert

Send us a message and we'll follow up within 1 hour — or book a free 30-min consultation directly.

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info@acemq.com
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Miami, FL 33130

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