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Databases · head to head

Apache Hadoop vs dbt

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
dbt logo

dbt

Databases

SQL transformation framework enabling analytics engineers to version, test and deploy models

From
Free
Free plan
Yes
Visit dbtFull review

The short version

  • dbt is a transformation framework.
  • Apache Hadoop is free; dbt starts at $100/user/mo.
  • Where dbt falls short: free tier severely limited: 3,000 models/month cap and single project maximum restricts team and production use.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Hadoop and dbt differ
AttributeApache Hadoopdbt
Pricing modelOpen-sourceSubscription|free
PlatformsWebWeb, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf)

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

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 dbt

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

dbt

  • Data warehouse transformation and ELT pipelinesnot Apache Hadoop
  • Analytics engineering for reporting and business intelligencenot Apache Hadoop
  • Data quality testing and validation at scalenot Apache Hadoop
  • Cross-functional data collaboration with version controlnot Apache Hadoop
  • Cost optimisation of warehouse usage through intelligent schedulingnot 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

dbt

  • Free tier severely limited: 3,000 models/month cap and single project maximum restricts team and production use
  • Starter at $1,200/year per seat: minimum 5-seat team costs $6,000/year baseline; no single-seat or 2-seat paid option
  • Enterprise pricing opaque: 'custom pricing' with no budget range for startups vs. enterprises; requires sales consultation
  • Model volume metering unclear: 'successful models/month' as a limit is ambiguous; unclear if this counts transformation runs, test runs, or deployment attempts

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

dbt

Free
  • DeveloperFree
    • Browser-based IDE
    • MFA
    • job scheduling
  • Starter$100/user/mo
    • dbt Wizard
    • basic Catalog
    • Semantic Layer
  • EnterpriseOn request
    • Advanced Catalog
    • Semantic Layer
    • dbt Copilot
  • Enterprise+On request
    • All Enterprise features
    • PrivateLink
    • IP restrictions

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 dbt if

  • You want to start without paying.
  • You work on Web, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf).
dbt review

Questions people ask

Is Apache Hadoop or dbt better?
Neither clearly leads. Apache Hadoop starts at Free and dbt at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or dbt?
Apache Hadoop starts at Free and dbt at Free.
Does Apache Hadoop or dbt run on more platforms?
Apache Hadoop runs on Web. dbt runs on Web, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf).
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 dbt is typically brought in for.
What can Apache Hadoop do that dbt 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.

dbt: How much does dbt cost?

dbt Developer is free. dbt Starter is $100/seat/month with a 5-seat minimum (or custom annual billing). Enterprise and Enterprise+ have custom pricing and require contacting sales.

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.

dbt: What is included in the free Developer plan?

The Developer plan is free and includes 1 developer seat, 3,000 successful models/month limit, 1 project, browser-based IDE, MFA, and job scheduling.

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.

dbt: What is the model limit on each plan?

Developer tier allows 3,000 successful models/month. Starter allows 15,000/month. Enterprise and Enterprise+ allow 100,000/month.

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.

dbt: Can I upgrade or downgrade my dbt plan?

Yes. dbt allows you to 'upgrade or downgrade at any time.' Starter plans bill monthly by credit card based on seat count; Enterprise plans are annual invoicing.

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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