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Technology · head to head

Apache Hadoop vs Sourcegraph

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
Free plan
Yes
Full reviewWebsite
Sourcegraph logo

Sourcegraph

Developer Tools

Code search and intelligence platform for large codebases with Cody AI assistant

From
$16000/year
Free plan
No
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.; Sourcegraph enterprise pricing starting at $16k annually creates significant barrier for small teams
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Hadoop and Sourcegraph differ
AttributeApache HadoopSourcegraph
Starting priceFree$16000/year
Pricing modelopen-sourcesubscription
Free tierYesNo
PlatformsWebCloud, Self-hosted
CategoryTechnologyDeveloper Tools

Identical on both: user rating (Not yet rated).

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 Sourcegraph

Nothing recorded that Apache Hadoop does not also cover.

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

Sourcegraph

  • Large-scale code migrations across hundreds of repositoriesnot Apache Hadoop
  • Enterprise governance and code ownership trackingnot Apache Hadoop
  • AI-assisted bug hunting and security pattern detectionnot Apache Hadoop
  • Onboarding engineers into large unfamiliar codebasesnot Apache Hadoop
  • Regulatory compliance and code audit trailsnot 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.

Sourcegraph

  • Enterprise pricing starting at $16k annually creates significant barrier for small teams
  • No free tier or standard plan published; all options require custom sales engagement
  • Complex enterprise-only positioning limits adoption compared to GitHub Copilot's pervasiveness
  • Requires indexing entire codebase; not suitable for small or ephemeral projects

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Sourcegraph

$16000/year
  • Enterprise$16000/year
    • Scales with team size
    • Credits for AI features per user
    • Deep Search

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 Sourcegraph if

  • You work on Cloud, Self-hosted.
Sourcegraph review

Questions people ask

Is Apache Hadoop or Sourcegraph better?
Neither clearly leads. Apache Hadoop starts at Free and Sourcegraph at $16000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Sourcegraph?
Apache Hadoop has a free tier; the other does not. Paid plans start at Free for Apache Hadoop and $16000/year for Sourcegraph.
Does Apache Hadoop or Sourcegraph run on more platforms?
Apache Hadoop runs on Web. Sourcegraph runs on Cloud, Self-hosted.
Can I use Apache Hadoop for free?
Yes. Apache Hadoop has a free tier, so you can try it without paying. Sourcegraph starts at $16000/year.
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 Sourcegraph is typically brought in for.
What can Apache Hadoop do that Sourcegraph cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability.

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.

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.

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.

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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