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Technology · head to head

Apache Hadoop vs Greenhouse

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
Greenhouse logo

Greenhouse

Technology

Hiring software for growing companies

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Apache Hadoop has a free tier, so it costs nothing to try first.
  • 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.; Greenhouse core plan lacks talent discovery and contact lookups
  • They diverge on capability: Apache Hadoop covers HDFS, Greenhouse covers Applicant tracking.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Hadoop and Greenhouse differ
AttributeApache HadoopGreenhouse
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsWebWeb, Ios, Android, Api
FoundedUnknown2012

Identical on both: 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 Greenhouse

  • Applicant tracking
  • Interview scheduling
  • Scorecard system
  • Job board posting
  • Candidate CRM
  • Reporting & analytics
  • Offer management
  • EEO compliance

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

Greenhouse

  • Enterprise hiring and recruiting automationnot Apache Hadoop
  • Multi-location and multi-team talent acquisitionnot Apache Hadoop
  • Structured interview process managementnot 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.

Greenhouse

  • Core plan lacks talent discovery and contact lookups
  • Core plan lacks email automation and applicant texting
  • Plus plan lacks resume anonymisation and application limits
  • Plus plan lacks audit logging and developer tools
  • Pricing customised by hiring volume and company size, not published
  • Only Pro tier offers audit logs and developer sandbox

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Greenhouse

On request

No published plan breakdown. See the Greenhouse review.

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 Greenhouse if

  • You need applicant tracking.
  • You work on Web, Ios, Android, Api.
  • You also want interview scheduling.
Greenhouse review

Questions people ask

Is Apache Hadoop or Greenhouse better?
Neither clearly leads. Apache Hadoop starts at Free and Greenhouse at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Greenhouse?
Apache Hadoop has a free tier; the other does not. Paid plans start at Free for Apache Hadoop and On request for Greenhouse.
Does Apache Hadoop or Greenhouse run on more platforms?
Apache Hadoop runs on Web. Greenhouse runs on Web, Ios, Android, Api.
Can I use Apache Hadoop for free?
Yes. Apache Hadoop has a free tier, so you can try it without paying. Greenhouse starts at On request.
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 Greenhouse is typically brought in for.
What can Apache Hadoop do that Greenhouse cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Greenhouse covers Applicant tracking, Interview scheduling, Scorecard system, Job board posting.

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.

Greenhouse: How much does Greenhouse cost?

Greenhouse does not publish specific pricing. The company states that pricing is customized based on your hiring needs, hiring volume, organizational complexity, and required features.

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.

Greenhouse: What are the Greenhouse pricing tiers?

Greenhouse offers three plan levels: Core (basic hiring structure), Plus (multi-location optimization), and Pro (complex enterprise hiring). Exact pricing requires contacting Greenhouse for a custom quote.

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.

Greenhouse: Do all Greenhouse tiers include the same features?

No. Core includes sourcing and scheduling, Plus adds automation and texting, and Pro adds enterprise data configuration and audit logs. All tiers can be customized.

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.

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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