Skip to main content

Apache Hadoop: Distributed Storage & Big Data Processing

Learn HDFS, MapReduce, YARN, and the broader Hadoop ecosystem with clear, practical guides for building and operating large-scale data platforms.

Get Started with HadoopRead the Blog
3×Default HDFS replication for fault tolerance
PB+Petabyte-scale storage across commodity nodes
20+In-depth guides on the Hadoop ecosystem
Easy to Use

Scalable Distributed Storage

HDFS (Hadoop Distributed File System) stores data across thousands of nodes, providing fault tolerance and high throughput access to large datasets.

Focus on What Matters

Parallel Data Processing

MapReduce enables processing of massive datasets in parallel across a cluster, breaking complex jobs into manageable Map and Reduce tasks.

Powered by React

Resource Management with YARN

YARN (Yet Another Resource Negotiator) dynamically allocates cluster resources, allowing multiple frameworks to share the same Hadoop cluster.

Popular Hadoop Guides

Start with the most-read tutorials and comparisons from our library.

What Is a Hadoop Cluster?

Architecture, node roles, and how a cluster fits together.

Read guide →

Hive vs Presto/Trino

Choosing the right SQL-on-Hadoop engine for your workloads.

Read guide →

HBase vs Cassandra

A practical NoSQL comparison for big data storage.

Read guide →

Hadoop vs Snowflake

Performance and cost trade-offs for modern data platforms.

Read guide →

Hadoop Alternatives

When to use Spark, Flink, cloud object storage, and more.

Read guide →

Hadoop 3 Features Deep Dive

Erasure coding, YARN improvements, and what changed.

Read guide →

Frequently Asked Questions

What is Apache Hadoop?

Apache Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware, built around HDFS, MapReduce, and YARN.

What are the core components of Hadoop?

The core components are HDFS for distributed storage, MapReduce for parallel processing, and YARN for cluster resource management.

Is Hadoop still relevant in 2026?

Yes. While cloud data platforms have grown, HDFS, YARN, and the wider Hadoop ecosystem remain widely used for large-scale, cost-effective on-premises and hybrid data processing.