What Hadoop does

Apache Hadoop is an open-source software framework originally established in 2006 inside the Apache Software Foundation and maintained as a community project under the Apache License 2.0. It provides four core modules — Hadoop Common, the Hadoop Distributed File System (HDFS), Hadoop YARN, and Hadoop MapReduce — that together enable reliable, scalable distributed processing of large data sets across clusters of machines, with fault tolerance handled at the application layer rather than via hardware redundancy.

The framework is complemented by a substantial ecosystem of related Apache projects including HBase (distributed NoSQL), Hive (data warehouse), Spark (general compute engine), Pig (data-flow language), Ozone (object store), Ambari (cluster management), ZooKeeper (coordination), Avro (serialization), Cassandra, Tez (DAG execution), Mahout (machine learning), and Submarine (unified AI/ML platform). Native connectors exist for major cloud object stores (AWS S3, Azure ABFS/ADLS, Aliyun OSS, Tencent COS, Huawei OBS), allowing the same HDFS/YARN abstractions to operate against cloud-resident data. The framework scales from a single server to thousands of machines and offers a Java API plus a WebHDFS REST API.

The business model is purely open-source distribution: the project generates no direct revenue, has no commercial license, no pricing tiers, and no enterprise sales motion; it is distributed via Apache download mirrors, Maven Central, and the Apache GitBox repository. All investment in development is contributed by volunteer committers, PMC members, and sponsoring organizations, with the foundation itself operating as a U.S.-incorporated nonprofit. The user base spans enterprise data teams, data engineering groups, and academic/research institutions globally.

Name

Hadoop

Legal name

Apache Hadoop

Website

https://hadoop.apache.org

Company type

Private

Founded year

2006

Operating status

Operating

Headcount range

11–50 employees

Short description

Apache Hadoop is an open-source distributed-computing framework under the Apache Software Foundation that provides HDFS storage, YARN resource management, and MapReduce processing across clusters, with cloud connectors and an ecosystem spanning Spark, Hive, HBase, Ozone, and Submarine for global enterprise and research use under Apache License 2.0.

Ownership category

akta.pro rank

Product category

Distributed Computing Framework

NAICS

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182)

SIC

Services-Prepackaged Software (7372)

akta.pro primary industry

NoSQL Database Management Systems (HDAEAAAB)

akta.pro secondary industries

Relational Database Management Systems (RDBMS) (HDAEAAAA), Cloud Networking Core Services (VPC/VNet, Load Balancing, DNS, NAT) (HDABAAAJ)

Keywords

• Distributed computing framework
• Big data processing
• Distributed file system
• Cluster resource management
• Open source data infrastructure

Headquarters

HQ city

Baltimore

HQ country

United States

HQ region

North America

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D, Personnel, Operations, Infrastructure

Distribution channels3 records

Marketing channels4 records

Core offering

Apache Hadoop is an open-source software framework that enables the distributed processing and storage of large data sets across clusters of computers using simple programming models. It scales from a single server to thousands of machines, delivers high availability through application-layer fault tolerance rather than hardware redundancy, and is composed of four core modules: Hadoop Common, HDFS, YARN, and MapReduce. The framework is released under the Apache License 2.0 and is freely downloadable for research and production use.

Product overview

Apache Hadoop is an open-source software framework consisting of four core modules (Hadoop Common, HDFS, YARN, and MapReduce) that work together to provide reliable, scalable, distributed computing. The framework is designed to scale from single servers to thousands of machines, each offering local computation and storage, with fault tolerance handled at the application layer rather than relying on hardware. The Hadoop ecosystem extends the core framework with numerous related projects including HBase (NoSQL database), Hive (data warehouse with SQL querying), Spark (fast compute engine), Ozone (object store), Pig (data-flow language), Ambari (cluster management), ZooKeeper (coordination service), Mahout (machine learning), Submarine (unified AI platform for ML/DL workloads), and Tez (generalized data-flow framework). These components form a comprehensive big data processing platform used for research and production across a wide variety of organizations.

Differentiator

Problem solved

Functional benefit

Brands

• Hadoop Common: The common utilities that support the other Hadoop modules.
• Hadoop Distributed File System (HDFS)
• Hadoop YARN
• Hadoop MapReduce

Products and services

• Apache Hadoop Open-source software framework for reliable, scalable, distributed computing. Enables distributed processing of large data sets across clusters using simple programming models, scales from single servers to thousands of machines, and provides fault tolerance at the application layer for enterprise data teams, research institutions, and data engineering teams.

Quantifiable outcome

• Scales to thousands of machines with local computation and storage per node

Segments3 records

Ideal customer profiles3 records

Technology focussed Yes

API detail

Has API

Yes

API docs

API detail

Core technology

AI maturity

App detail

Integration10 records

Feature5 records

Scale indicators3 records

Recent moves6 records

Expansion highlights4 records

Broad incumbents

• Databricks: Unified data analytics platform built on Apache Spark, often deployed alongside or as an alternative to Hadoop for data engineering, ML, and analytics workloads.
• Amazon EMR: Managed Hadoop framework service on AWS that monetizes Hadoop as cloud infrastructure, supporting Hadoop, Spark, HBase, and Presto on EC2 clusters.
• Cloudera: Commercial Hadoop distribution provider (formed from Cloudera-Hortonworks merger) offering enterprise support, management tools, and data platform services built on Apache Hadoop.
• Snowflake: Cloud-native data warehouse platform that competes with Hive-on-Hadoop and other Hadoop-based SQL query workloads, offering simpler managed experience and separation of storage and compute.
• Google Cloud Dataproc: Managed Spark and Hadoop service on Google Cloud, providing similar managed Hadoop capabilities as AWS EMR with integration to GCP storage and BigQuery.

Direct peers

• Apache Flink: Distributed stream and batch processing framework that competes directly with Hadoop MapReduce/YARN for large-scale data processing workloads, particularly in real-time streaming use cases.
• Apache Kafka: Distributed streaming platform commonly paired with Hadoop in modern data architectures, providing real-time event ingestion that complements Hadoop's batch processing strengths.
• Apache Spark: Fast general compute engine that runs on Hadoop YARN and provides ETL, machine learning, stream processing, and graph computation. Same Apache ecosystem, often deployed together on shared Hadoop infrastructure.

Emerging players

• Dremio: Data lakehouse platform that provides SQL querying and semantic layer on data lake storage, competing with Hive and other Hadoop-based query engines for modern lakehouse workloads.
• Trino (formerly Presto): Distributed SQL query engine for big data that competes with Hive for interactive querying on Hadoop data lakes, with growing adoption for federated queries across data sources.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat5 records

Key risks5 records

Key highlights7 records

Customer concentration

• GitHubgithub.com/apache/hadoop
• Xx.com/hadoop
• LinkedInlinkedin.com/company/hadoop

Revenue estimate

Valuation estimate

Number of profiles

Funding overview

Funding rounds

Investors

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M&A

Investments

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Frequently asked questions about Hadoop