Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
Let’s take a look at what some of those terms mean.
- Open-source software. Open-source software is created and maintained by a network of developers from around the globe. It’s free to download, use and contribute to, though more and more commercial versions of Hadoop are becoming available.
- Framework. In this case, it means that everything you need to develop and run software applications is provided – programs, connections, etc.
- Massive storage. The Hadoop framework breaks big data into blocks, which are stored on clusters of commodity hardware.
- Processing power. Hadoop concurrently processes large amounts of data using multiple low-cost computers for fast results.
What are the benefits of Hadoop?
One of the top reasons that organizations turn to Hadoop is its ability to store and process huge amounts of data – any kind of data – quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things, that’s a key consideration. Other benefits include:
- Computing power. Its distributed computing model quickly processes big data. The more computing nodes you use, the more processing power you have.
- Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
- Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. And it automatically stores multiple copies of all data.
- Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
- Scalability. You can easily grow your system simply by adding more nodes. Little administration is required.
A little bit of Hadoop history
It all started with the World Wide Web. As the web grew in the late 1900s and early 2000s, search engines and indexes were created to help locate relevant information amid the text-based content. In the early years, search results really were returned by humans. But as the web grew from dozens to millions of pages, automation was needed. Web crawlers were created, many as university-led research projects, and search engine start-ups took off (Yahoo, AltaVista, etc.).
One such project was an open-source web search engine called Nutch – the brainchild of Doug Cutting and Mike Cafarella. They wanted to invent a way to return web search results faster by distributing data and calculations across different computers so multiple tasks could be accomplished simultaneously. During this time, another search engine project called Google was in progress. It was based on the same concept – storing and processing data in a distributed, automated way so that relevant web search results could be returned faster.
In 2006, Cutting joined Yahoo and took with him the Nutch project as well as ideas based on Google’s early work with automating distributed data storage and processing. The Nutch project was divided. The web crawler portion remained as Nutch. The distributed computing and processing portion became Hadoop (named after Cutting’s son’s toy elephant). In 2008, Yahoo released Hadoop as an open-source project. Today, Hadoop’s framework and ecosystem of technologies are managed and maintained by the non-profit Apache Software Foundation (ASF), a global community of software developers and contributors.
What components make up Hadoop?
Currently, four core modules are included in the basic framework from the Apache Foundation:
- Hadoop Common – the libraries and utilities used by other Hadoop modules.
- Hadoop Distributed File System (HDFS) – the Java-based scalable system that stores data across multiple machines without prior organization.
- MapReduce – a software programming model for processing large sets of data in parallel.
- YARN – resource management framework for scheduling and handling resource requests from distributed applications. (YARN is an acronym for Yet Another Resource Negotiator.)
Other software components that can run on top of or alongside Hadoop and have achieved top-level Apache project status include:
- Pig – a platform for manipulating data stored in HDFS that includes a compiler for MapReduce programs and a high-level language called Pig Latin. It provides a way to perform data extractions, transformations and loading, and basic analysis without having to write MapReduce programs.
- Hive – a data warehousing and SQL-like query language that presents data in the form of tables. Hive programming is similar to database programming. (It was initially developed by Facebook.)
- HBase – a nonrelational, distributed database that runs on top of Hadoop. HBase tables can serve as input and output for MapReduce jobs.
- HCatalog – a table and storage management layer that helps users share and access data.
- Ambari – a web interface for managing, configuring and testing Hadoop services and components.
- Cassandra – A distributed database system.
- Chukwa – a data collection system for monitoring large distributed systems.
- Flume – software that collects, aggregates and moves large amounts of streaming data into HDFS.
- Oozie – a Hadoop job scheduler.
- Sqoop – a connection and transfer mechanism that moves data between Hadoop and relational databases.
- Spark – an open-source cluster computing framework with in-memory analytics.
- Solr – an scalable search tool that includes indexing, reliability, central configuration, fail-over and recovery.
- Zookeeper – an application that coordinates distributed processes.