Hadoop

Open-source software for reliable, scalable, distributed computing.

Big data

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About Hadoop

As Hadoop is an open source project, you can find more open source alternatives and stats on LibHunt.

Key features

Scalability

Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.

Cost-Effective

It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.

Fault Tolerance

Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.

Flexibility

It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.

Parallel Processing

Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.

Community Support

As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Alternatives to consider

Community ratings & full list

Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Apache Cassandra

The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.

Apache Kafka

Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

MongoDB

MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

PostgreSQL

PostgreSQL is a powerful, open source object-relational database system.

Resources

GitHub

Pricing summary

Model

Open source

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