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Apache Hadoop vs Teradata

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
Teradata logo

Teradata

Databases

Long-established enterprise MPP data warehouse, rebranded in 2026 as the Autonomous Knowledge Platform, sold for cloud, on-premises and hybrid.

From
On request
Free plan
No
Visit TeradataFull review

The short version

  • Apache Hadoop is free; Teradata prices on request.
  • Only Apache Hadoop lists rack awareness; only Teradata lists massively parallel architecture.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Hadoop and Teradata differ
AttributeApache HadoopTeradata
Starting priceFreeOn request
Pricing modelOpen-sourceQuote
Free tierYesNo

Identical on both: platforms (Web), 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 Teradata

  • Massively parallel architecture
  • Workload management
  • Mature cost-based optimiser
  • Cloud, on-premises and hybrid
  • Bulk load utilities
  • BTEQ scripting
  • In-database analytics
  • Enterprise Vector Store

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

Teradata

  • A large existing Teradata estate where the practical question is which workloads to migrate first rather than whether to adoptnot Apache Hadoop
  • High-concurrency mixed workloads where hundreds of analysts and scheduled jobs contend and predictable prioritisation matters more than peak single-query speednot Apache Hadoop
  • Regulated reporting where the same query must produce the same answer for years and the audit trail of the existing implementation has valuenot Apache Hadoop
  • Hybrid deployments where regulatory or data-residency rules keep a portion of the warehouse on-premises while the rest moves to cloudnot 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

Teradata

  • Licensing is negotiated rather than published, so there is no way to compare total cost against a consumption-priced warehouse without entering a sales cycle, and the comparison is only ever as good as the workload profile you gave them
  • The SQL dialect and the loading utilities are Teradata-specific, so every stored procedure, macro and BTEQ script written against the platform is migration debt that grows with each release you ship
  • Primary index choice determines data distribution, and a poorly chosen index concentrates rows on a few processing units, which surfaces as one slow query rather than an error and needs a specialist to diagnose
  • The skills market is contracting, so DBA and workload-management expertise is expensive to hire, hard to replace when someone retires, and increasingly hard to buy from consultancies whose own bench has moved to cloud warehouses
  • The 2026 renaming of Vantage, VantageCloud, ClearScape and QueryGrid split documentation, runbooks and vendor material across two naming systems, so searching for an error or a configuration now returns results for a product that is described under a different name

Pricing, plan by plan

Apache Hadoop

Free

No published plan breakdown. See the Apache Hadoop review.

Teradata

On request

No published plan breakdown. See the Teradata 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 Teradata if

  • You need massively parallel architecture.
  • You also want workload management.
Teradata review

Questions people ask

Is Apache Hadoop or Teradata better?
Neither clearly leads. Apache Hadoop starts at Free and Teradata at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Hadoop or Teradata?
Apache Hadoop has a free tier; the other does not. Paid plans start at Free for Apache Hadoop and On request for Teradata.
Does Apache Hadoop or Teradata run on more platforms?
Both run on Web, so platform support will not decide this one for you.
Can I use Apache Hadoop for free?
Yes. Apache Hadoop has a free tier, so you can try it without paying. Teradata starts at On request.
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 Teradata is typically brought in for.
What can Apache Hadoop do that Teradata cannot?
Apache Hadoop covers HDFS, YARN, MapReduce, HDFS federation and high availability. Teradata covers Massively parallel architecture, Workload management, Mature cost-based optimiser, Cloud, on-premises and hybrid.

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.

Teradata: Is Teradata only on-premises?

No. It is sold for cloud, on-premises and hybrid deployment, and the cloud offering is now branded Teradata Cloud. A large part of the installed base is still on-premises or hybrid.

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.

Teradata: How does it compare to Snowflake or BigQuery?

On raw elasticity and cost transparency the cloud warehouses win. On mixed-workload concurrency management against a large existing query estate Teradata is still hard to replace, which is why migrations off it take years rather than quarters.

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.

Teradata: Why do organisations stay on it?

Because the cost of leaving is the estate, not the data. Thousands of procedures, scripts and extracts written in a proprietary dialect have to be rewritten and revalidated, and in regulated reporting that revalidation is the expensive part.

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.

Teradata: What changed in the 2026 rebrand?

Vantage became the Autonomous Knowledge Platform, VantageCloud became Teradata Cloud, ClearScape Analytics became AI Studio and QueryGrid became Fabric. The underlying products are continuous with what came before.

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.

Teradata: Can it handle AI and vector workloads?

It has added an Enterprise Vector Store and in-database analytics branded AI Studio. Whether that is preferable to moving the data into a purpose-built vector store depends on how much of your data already lives in the warehouse.

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