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Technology · head to head

Apache Hadoop vs Canny

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
Canny logo

Canny

Technology

Capture, organize, and analyze product feedback

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.; Canny tracked user pricing model causes costs to balloon as product grows and engagement increases, creating perverse incentive where more successful feedback gathering means higher costs
  • They diverge on capability: Apache Hadoop covers HDFS, Canny covers Feedback boards.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Hadoop and Canny differ
AttributeApache HadoopCanny
Pricing modelopen-sourcesubscription
PlatformsWebWeb, Claude AI (MCP)
FoundedUnknown2015

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 Canny

  • Feedback boards
  • Voting system
  • Roadmap planning
  • Changelog
  • User segmentation
  • Status updates
  • Admin moderation
  • Analytics

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 Canny
  • Running Spark or Flink under YARN on hardware you already own, using HDFS as the storage layernot Canny
  • Keeping long-lived regulated archives on infrastructure entirely within your own data centres and legal jurisdictionnot Canny
  • Maintaining legacy Hive and MapReduce workloads during a staged migration to a lakehouse or cloud platformnot Canny

Canny

  • Feature request managementnot Apache Hadoop
  • Product roadmappingnot Apache Hadoop
  • Customer feedback collectionnot Apache Hadoop
  • Changelog communicationnot Apache Hadoop
  • User engagementnot 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.

Canny

  • Tracked user pricing model causes costs to balloon as product grows and engagement increases, creating perverse incentive where more successful feedback gathering means higher costs
  • Limited integrations on Core plan; must upgrade to Pro to connect with Jira and Linear
  • Lacks built-in analytics for identifying themes and patterns across hundreds of feedback requests without manual tagging
  • Customizations limited for public-facing interfaces regarding branding and information presentation
  • Autopilot AI cannot be linked to product knowledge bases to better understand products and improve function
  • Slow response times reported by users, affecting feedback management efficiency
  • Limited custom user fields and manual data updates require API access

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Canny

Free
  • FreeFree
    • Up to 100 tracked users
    • Unlimited feedback
    • 1 board
  • Starter$400/month
    • 1,000 tracked users
    • Unlimited boards
    • Private boards
  • Growth$900/month
    • 5,000 tracked users
    • API access
    • SSO
  • Business$undefined/month
    • Unlimited tracked users
    • White label
    • SLA

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 Canny if

  • You need feedback boards.
  • You want to start without paying.
  • You work on Web, Claude AI (MCP).
  • You also want voting system.
Canny review

Questions people ask

Is Apache Hadoop or Canny better?
Neither clearly leads. Apache Hadoop starts at Free and Canny at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Canny?
Apache Hadoop starts at Free and Canny at Free.
Does Apache Hadoop or Canny run on more platforms?
Apache Hadoop runs on Web. Canny runs on Web, Claude AI (MCP).
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 Canny is typically brought in for.
What can Apache Hadoop do that Canny cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Canny covers Feedback boards, Voting system, Roadmap planning, Changelog.

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.

Canny: Does Canny offer a free plan?

Yes. Canny offers a free plan that allows collecting up to 100 feedback ideas. Free users get basic features without roadmap, changelog, or integrations.

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.

Canny: How is Canny priced?

Canny pricing scales based on tracked users. Core starts at $19/month (100 users, annual), Pro starts at $79/month (100 users, annual). Prices increase as tracked users grow.

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.

Canny: What is a tracked user in Canny?

A tracked user is anyone who posts, votes, or comments on your Canny board or embedded widget. Each unique end user counts once and the count accumulates.

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.

Canny: What is Canny Autopilot?

Canny Autopilot is an AI feature that automatically captures feature requests from communication tools like Gong, Intercom, Slack, and Zendesk. It prioritizes requests by revenue impact.

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.

Canny: Does Canny have a public roadmap feature?

Yes. Canny provides a public roadmap where customers can see what is planned, building, and shipped. Customers can vote on features and watch their feedback progress.

Source
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