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Apache Hadoop vs Redis

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
Redis logo

Redis

Technology

The real-time data platform

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.; Redis licensing changed from BSD to AGPL in 2025, impacting open-source usage
  • They diverge on capability: Apache Hadoop covers HDFS, Redis covers In-memory data store.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Hadoop and Redis differ
AttributeApache HadoopRedis
Pricing modelopen-sourceUnknown
PlatformsWebLinux, macOS, Windows
FoundedUnknown2009

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 Redis

  • In-memory data store
  • Data structures
  • Pub/Sub messaging
  • Lua scripting
  • Transactions
  • Persistence options
  • Replication
  • Clustering

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

Redis

  • Cachingnot Apache Hadoop
  • Session managementnot Apache Hadoop
  • Real-time analyticsnot Apache Hadoop
  • Message queuingnot Apache Hadoop
  • Leaderboardsnot 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.

Redis

  • Licensing changed from BSD to AGPL in 2025, impacting open-source usage
  • All data must fit in memory, limiting scalability to available RAM
  • No built-in support for multi-tenancy
  • Limited transaction support compared to traditional databases

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Redis

Free

No published plan breakdown. See the Redis 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 Redis if

  • You need in-memory data store.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want data structures.
Redis review

Questions people ask

Is Apache Hadoop or Redis better?
Neither clearly leads. Apache Hadoop starts at Free and Redis at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Redis?
Apache Hadoop starts at Free and Redis at Free.
Does Apache Hadoop or Redis run on more platforms?
Apache Hadoop runs on Web. Redis runs on Linux, macOS, Windows.
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 Redis is typically brought in for.
What can Apache Hadoop do that Redis cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Redis covers In-memory data store, Data structures, Pub/Sub messaging, Lua scripting.

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.

Redis: Is Redis open source?

Redis was open source under the BSD license since its inception in 2009 and has remained open source. However, in 2024-2025, Redis Labs changed licensing to source-available and AGPL, prompting the creation of Valkey, a BSD-licensed open-source fork.

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.

Redis: What is Redis used for?

Redis is an in-memory data structure store used primarily as a cache, database, and message broker. It provides high-speed data access for real-time applications, sessions, leaderboards, real-time analytics, and other use cases requiring fast data retrieval.

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.

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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