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Apache Hadoop vs MongoDB

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
MongoDB logo

MongoDB

Technology

The developer 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.; MongoDB 16 MB maximum document size limits large single objects
  • They diverge on capability: Apache Hadoop covers HDFS, MongoDB covers Document model.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Hadoop and MongoDB differ
AttributeApache HadoopMongoDB
Pricing modelopen-sourceUnknown
PlatformsWebCloud (Atlas), Self-hosted, Multi-cloud (AWS, Google Cloud, Azure)
FoundedUnknown2007

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 MongoDB

  • Document model
  • Distributed architecture
  • ACID transactions
  • Real-time analytics
  • Full-text search
  • Time series data
  • Geospatial queries
  • Aggregation framework

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

MongoDB

  • Mobile applicationsnot Apache Hadoop
  • Content managementnot Apache Hadoop
  • Real-time analyticsnot Apache Hadoop
  • IoT applicationsnot Apache Hadoop
  • Gaming backendsnot 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.

MongoDB

  • 16 MB maximum document size limits large single objects
  • No native JOIN support for relational data operations
  • Higher memory usage due to storing field names with each document
  • Eventual consistency in distributed deployments can cause data stale reads

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

MongoDB

Free
  • M0Free
    • 512 MB storage
    • Learning and exploration
  • M2$9/month
    • 2 GB storage
    • Development and testing
  • M5$25/month
    • 5 GB storage
  • M10+$56.94/month
    • Dedicated clusters for production

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 MongoDB if

  • You need document model.
  • You want to start without paying.
  • You work on Cloud (Atlas), Self-hosted, Multi-cloud (AWS, Google Cloud, Azure).
  • You also want distributed architecture.
MongoDB review

Questions people ask

Is Apache Hadoop or MongoDB better?
Neither clearly leads. Apache Hadoop starts at Free and MongoDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or MongoDB?
Apache Hadoop starts at Free and MongoDB at Free.
Does Apache Hadoop or MongoDB run on more platforms?
Apache Hadoop runs on Web. MongoDB runs on Cloud (Atlas), Self-hosted, Multi-cloud (AWS, Google Cloud, Azure).
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 MongoDB is typically brought in for.
What can Apache Hadoop do that MongoDB cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. MongoDB covers Document model, Distributed architecture, ACID transactions, Real-time analytics.

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.

MongoDB: Does MongoDB offer a free tier?

Yes. MongoDB Atlas offers an M0 free tier with 512 MB storage for learning and exploration, plus paid options starting at $9/month for M2 with 2 GB storage.

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.

MongoDB: Can I self-host MongoDB?

Yes. You can run MongoDB Community Edition on your own servers, or use MongoDB Enterprise Advanced for self-managed production deployments with enterprise features.

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.

MongoDB: What is the maximum document size in MongoDB?

Documents are limited to 16 MB. For documents exceeding this limit, you can use MongoDB's GridFS API to store files larger than the maximum size.

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.

MongoDB: Does MongoDB support ACID transactions?

Yes. MongoDB supports ACID transactions within a single document by default, and multi-document ACID transactions are available for replica sets and sharded clusters in MongoDB 4.0+.

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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