Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. Developed by Apache Software Foundation, it allows for the reliable, scalable, and distributed processing of large data sets across clusters of commodity hardware.
History and Development
It was created by Doug Cutting, an engineer at Yahoo and named after his son's toy elephant. In 2005, Cutting started developing the framework to address problems around storing and processing petabytes worth of data across thousands of commodity servers for various applications at Yahoo. In 2006, Yahoo open sourced it and the project soon gained popularity. The Apache Software Foundation incubator took over Hadoop project in 2006. It saw its first stable release, version 0.20.205, in 2008. Today, it is one of the most widely used open-source big data technologies.
Key Components of Framework
It consists of the Hadoop Distributed File System (HDFS) and MapReduce programming model as its core components. Hadoop provides a distributed filesystem that stores data across clusters of commodity hardware. MapReduce is a programming model for processing large datasets in a distributed manner across clusters.
HDFS - HDFS is a distributed file system that stores large data sets reliably and distributes them across clusters. It provides high throughput access to application data and is designed to be deployed on low-cost hardware. HDFS provides replication of data and handles failures transparently.
MapReduce - MapReduce is a processing engine for large scale data processing. It divides the work into independent chunks that are processed in parallel by the nodes in it cluster. MapReduce abstracts parallel and distributed programming and handles scheduling, monitoring jobs and tasks.
YARN - Yet Another Resource Negotiator (YARN) is a framework for job scheduling and cluster resource management. YARN improves on earlier MapReduce framework and allows it to support parallel execution of jobs as well as non-MapReduce compute paradigms.
Other Components - In addition to core components, it also consists of related projects like Pig, Hive, HBase, ZooKeeper etc. that provide data warehousing, data processing and NoSQL capabilities on top of HDFS and MapReduce.
Ecosystem and Applications
Over time, Hadoop ecosystem has expanded with a host of related projects, software stacks and platforms to support various workloads on it clusters. Popular platforms that leverage it for big data applications include:
- Apache Spark - A fast and general purpose cluster computing framework that provides in-memory primitives as well as support for streaming, machine learning, and graph processing workloads.
- Apache Flink - Another cluster computing framework aimed at stream processing use cases and provides capabilities like stream-stream joins, predictive analytics, machine learning, and graph processing on streaming data.
- Apache Hive - A data warehousing tool that provides SQL-like interface to query large datasets stored in its HDFS. Used for reporting and ad-hoc analysis on big datasets.
- Apache HBase - A distributed, scalable, big data store that provides random access and real-time read/write capability for massive amounts of structured and unstructured data.
- Apache Kafka - A distributed streaming platform and enterprise messaging system used for building real-time data pipelines and streaming applications on Hadoop.
- Apache Impala - Enables low-latency SQL queries to run directly on data stored in HDFS across nodes in a cluster. Support complex queries like joins, aggregation pushdowns etc. for interactive analytics.
This covers the key aspects of Apache Hadoop - one of the most important distributed data management platforms supporting big data applications across industries today. It powers analytics on hundreds of petabytes of data today in enterprises and its ecosystem continues to expand with emerging technologies. It provides an open-source platform to reliably store, process and gain insights from massive volumes of unstructured and structured data.
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