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Apache Hadoop

Distributed Storage and Processing Framework

About Apache Hadoop

Framework that allows for distributed processing of large datasets across clusters of computers using simple programming models. Designed to scale from single servers to thousands of machines, each offering local computation and storage. Uses HDFS for distributed storage and MapReduce for processing.

Key Features

1HDFS distributed file system for storing petabytes of data across commodity hardware
2MapReduce programming model for distributed batch data processing
3YARN resource manager for scheduling and managing cluster workloads
4Ecosystem of tools including Hive, Pig, HBase, Sqoop, and Oozie
5Fault tolerance through automatic data replication across cluster nodes

How Python Data Engineers Use Apache Hadoop

Python data engineers interact with Hadoop primarily via PySpark (which runs on YARN), Hive (using PyHive or impyla), and HDFS (using hdfs3 or fsspec). While Hadoop MapReduce has been largely replaced by Spark for new development, HDFS remains the storage layer for many on-premise data lakes. Python engineers use Hadoop ecosystem tools for legacy batch pipelines, Hive-based data warehouses, and large organisations with existing Hadoop infrastructure.

Frequently Asked Questions

What is Apache Hadoop used for?▾

Is Apache Hadoop free to use?▾

Yes, Apache Hadoop is free to use.

What category does Apache Hadoop belong to?▾

Apache Hadoop is listed under the Big Data Processing category on Python Data Engineering.

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Similar Big Data Processing Tools

3 tools

Tool	Pricing	Rating
AB Apache Beamfeatured Unified Batch and Stream Processing	Free	★ 4.5	→
AT Apache Tez DAG-Based Processing Framework	Free	★ 4.0	→
PR Prestofeatured Distributed SQL Query Engine	Free	★ 4.5	→