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Databases · head to head

Apache Hadoop vs DataGrip

Apache Hadoop logo

Apache Hadoop

Databases

The original open source framework for distributed storage and batch processing on commodity servers, now largely a legacy platform.

From
Free
Free plan
Yes
Visit Apache HadoopFull review
DataGrip logo

DataGrip

Databases

Cross-platform database IDE from JetBrains for SQL and NoSQL databases

From
Free
Free plan
Yes
Visit DataGripFull review

The short version

  • Apache Hadoop is free; DataGrip starts at $8.25/mo.
  • Only Apache Hadoop lists HDFS federation and high availability; only DataGrip lists intelligent SQL Completion.
  • Where DataGrip falls short: commercial use requires a paid subscription; the free tier is non-commercial only.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Hadoop and DataGrip differ
AttributeApache HadoopDataGrip
Pricing modelOpen-sourceSubscription
PlatformsWebWindows, macOS, Linux

Identical on both: starting price (Free), free tier (Yes), category (Databases).

Not yet recorded: founded for Apache Hadoop.

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 DataGrip

  • Intelligent SQL Completion
  • Schema Navigation
  • Data Editor
  • Version Control for Scripts
  • Multi-database Support

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

DataGrip

  • Writing and running SQL queries across multiple database enginesnot Apache Hadoop
  • Browsing and editing schema and table data visuallynot Apache Hadoop
  • Version-controlling database migration scriptsnot Apache Hadoop
  • Standardizing database tooling across a JetBrains-based teamnot 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

DataGrip

  • Commercial use requires a paid subscription; the free tier is non-commercial only
  • No built-in database administration features like backup scheduling found in dedicated DBA tools
  • Heavier resource footprint than lightweight single-purpose SQL clients
  • NoSQL support (e.g. MongoDB) is less mature than its relational database tooling

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

DataGrip

Free
  • Free (non-commercial)Free
    • Personal, non-commercial use only
  • Individual - Year 1$99/yr
    • Full DataGrip license
    • Free updates during subscription
  • Individual - Year 2+$79/yr
    • Continuity discount from second year onward

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 DataGrip if

  • You need intelligent sql completion.
  • You want to start without paying.
  • You work on Windows, macOS, Linux.
  • You also want schema navigation.
DataGrip review

Questions people ask

Is Apache Hadoop or DataGrip better?
Neither clearly leads. Apache Hadoop starts at Free and DataGrip at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or DataGrip?
Apache Hadoop starts at Free and DataGrip at Free.
Does Apache Hadoop or DataGrip run on more platforms?
Apache Hadoop runs on Web. DataGrip runs on Windows, macOS, Linux.
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 DataGrip is typically brought in for.
What can Apache Hadoop do that DataGrip cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. DataGrip covers Intelligent SQL Completion, Schema Navigation, Data Editor, Version Control for Scripts.

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.

DataGrip: Is DataGrip free for personal use?

JetBrains introduced a free non-commercial license for DataGrip in October 2025, allowing personal use, while commercial use still requires a paid annual or monthly subscription.

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.

DataGrip: Who qualifies for the free non-commercial license?

Only individuals are eligible, for uses like learning, open-source work, or content creation. Anyone paid by an employer, including at a non-profit, must use a commercial license instead.

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.

DataGrip: How long does the free non-commercial license last?

It lasts one year and auto-renews if DataGrip was used at least once in the final six months; otherwise you can simply reapply for a new license.

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.

DataGrip: Does the free license have fewer features than the paid version?

No, it is a full-featured IDE identical to the paid version, though it requires anonymized telemetry sharing that cannot be opted out of under the non-commercial agreement.

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.

DataGrip: Can I use DataGrip offline with the free license?

No, activation requires logging into a JetBrains Account; offline activation codes are not available for the free non-commercial license.

Source
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