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Technology · head to head

Apache Hadoop vs Jira

Apache Hadoop logo

Apache Hadoop

Technology

The original open source framework for distributed storage and batch processing on commodity servers, now largely a legacy platform.

From
Free
Rated
-
Full reviewWebsite
Jira logo

Jira

Technology

The #1 software development tool used by agile teams

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Hadoop the free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.; Jira free tier strictly limited to 10 users with only 2 GB storage; unsuitable for growing teams
  • They diverge on capability: Apache Hadoop covers HDFS, Jira covers Scrum boards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Hadoop and Jira actually diverge.

Attributes where Apache Hadoop and Jira differ
AttributeApache HadoopJira
Pricing modelopen-sourcesubscription
PlatformsWebCloud, Web
FoundedUnknown2002

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Technology).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Hadoop

  • HDFS
  • YARN
  • MapReduce
  • HDFS federation and high availability
  • Kerberos security
  • Rack awareness
  • S3A and object store connectors
  • Ecosystem compatibility

Only in Jira

  • Scrum boards
  • Kanban boards
  • Roadmaps
  • Agile reporting
  • Custom workflows
  • Issue tracking
  • Sprint planning
  • DevOps integration

What people use each for

The jobs each tool is most often brought in to do.

Apache Hadoop

  • Operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storagenot Jira
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Jira
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Jira
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Jira

Jira

  • Software development teams leveraging AI-powered Rovo for trend analysis and bottleneck identificationnot Apache Hadoop
  • Organisations requiring integrated issue tracking, project planning, and team alignment toolsnot Apache Hadoop
  • Enterprises with 1,000+ users needing advanced analytics, security controls, and custom deployment optionsnot Apache Hadoop

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Hadoop

  • The free vendor distributions no longer exist: Cloudera's CDH and Hortonworks' HDP have reached end of support and the successor CDP is subscription-only, so running Hadoop without paying now means assembling, testing and security-patching Apache releases yourself.
  • HDFS couples storage to compute, so adding capacity means buying whole nodes with CPU and memory you may not need, and the entire industry moved to object storage precisely because it lets the two be bought separately.
  • The NameNode holds all filesystem metadata in memory, so a cluster with tens of millions of small files exhausts heap long before it exhausts disk, and the remedy is a file compaction job that somebody has to write, schedule and own indefinitely.
  • Operating it is a distinct specialism covering Kerberos, YARN queue tuning, JVM garbage collection and the compatibility matrix between Hive, HBase, Ranger, Oozie and the core, and an upgrade touches all of them at once rather than one at a time.
  • MapReduce is maintained for compatibility rather than actively developed, and new work goes to Spark or Flink, so a job written against MapReduce today is written against an API that will not gain anything further.
  • Hiring is against you: the talent pool has moved to cloud data platforms over the past decade, so a Hadoop estate increasingly depends on a small number of individuals, which makes it a succession risk before it is a technical one.

Jira

  • Free tier strictly limited to 10 users with only 2 GB storage; unsuitable for growing teams
  • Per-seat pricing ($7.91–$14.54/user/month) accumulates significantly for large teams; 50-person team costs £3,955–7,270 monthly
  • Enterprise tier requires annual billing and sales contact; no transparent per-user pricing available
  • Premium features (cross-team planning, advanced automation) available only at $14.54/user/month tier or above

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Jira

Free
  • FreeFree
    • Up to 10 users
    • 2 GB storage
    • 100 automation rule runs per month
  • Standard$7.91/user/month
    • Up to 100,000 users per site
    • 250 GB storage
    • 1,700 automation rule runs per month
  • Premium$14.54/user/month
    • Unlimited storage
    • 1,000 rule runs per month per paid user
    • 70 credits per user monthly for Rovo
  • Enterprise$null/custom
    • 150 credits per user monthly for Rovo
    • Multiple sites (up to 150)
    • Unlimited automation rule runs

Which should you pick?

Choose Apache Hadoop if

  • You need hdfs.
  • You want to start without paying.
  • You also want yarn.
Apache Hadoop review

Choose Jira if

  • You need scrum boards.
  • You want to start without paying.
  • You work on Cloud, Web.
  • You also want kanban boards.
Jira review

Questions people ask

Is Apache Hadoop or Jira better?
Neither clearly leads. Apache Hadoop starts at Free and Jira at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Jira?
Apache Hadoop starts at Free and Jira at Free.
Does Apache Hadoop or Jira run on more platforms?
Apache Hadoop runs on Web. Jira runs on Cloud, Web.
Can I use Apache Hadoop for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Hadoop best used for?
Apache Hadoop is most often used for operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage, running spark or flink under yarn on hardware you already own, using hdfs as the storage layer, keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdiction, maintaining legacy hive and mapreduce workloads during a staged migration to a lakehouse or cloud platform. Of those, operating an existing multi-petabyte on-premises estate where data residency or egress costs rule out moving to cloud object storage and running spark or flink under yarn on hardware you already own, using hdfs as the storage layer are not what Jira is typically brought in for.
What can Apache Hadoop do that Jira cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Jira covers Scrum boards, Kanban boards, Roadmaps, Agile reporting.

Answered from the vendors’ own pages

Apache Hadoop: Is Hadoop dead?

No, but it is legacy. Large on-premises HDFS estates still run and are still supported, and Spark and Flink still run on YARN. What has ended is Hadoop as a default choice for new platforms, which now start on object storage.

Jira: What is Jira's free tier?

Jira's Free plan supports up to 10 users, includes 2 GB storage, allows 100 automation rule runs per month, and provides community support only. Ideal for small teams or evaluation.

Source
Apache Hadoop: Can I still get a free packaged distribution?

Not a maintained one. CDH and HDP reached end of support and Cloudera's CDP is a paid subscription. The remaining free route is building and patching Apache releases yourself, which is a real engineering commitment.

Jira: How much does Jira Standard cost?

Jira Standard costs $7.91 per user per month and includes 250 GB storage, 1,700 automation rule runs per month, Rovo Search/Chat/Agents, and 9/5 regional support with 2-hour response time for critical issues.

Source
Apache Hadoop: Do I need Hadoop to run Spark?

No. Spark runs standalone, on Kubernetes and on managed cloud services, and reads object storage directly. Many Spark deployments include Hadoop client libraries for the filesystem connectors without running a Hadoop cluster at all.

Jira: What is included in Jira Premium?

Jira Premium costs $14.54 per user per month and includes unlimited storage, 1,000 rule runs per user per month, 70 monthly Rovo credits, 24/7 support with 1-hour critical response, and 99.9% uptime SLA.

Source
Apache Hadoop: What replaced HDFS?

Object storage, typically S3 or a compatible system, combined with an open table format such as Apache Iceberg or Delta Lake. Apache Ozone exists as an object store within the Hadoop ecosystem for organisations staying on-premises.

Jira: Does Jira offer an Enterprise plan?

Yes, Jira Enterprise is available with custom pricing (contact sales). It includes up to 150 sites, 150 monthly Rovo credits per user, unlimited automation runs, 24/7 support with 30-minute critical response, and 99.95% uptime SLA.

Source
Apache Hadoop: Is it cheaper than the cloud?

It can be at multi-petabyte scale with steady, predictable utilisation, particularly where egress charges would be large. Include the staffing cost honestly, because the specialist operators a Hadoop cluster requires are scarce and therefore expensive.

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