Hadoop is a free, Java-based programming framework which processes large data sets in a distributed IT environment. Hadoop can run applications on systems with large data collections and produces quick data transfer rates among nodes. Hadoop was inspired by Google's MapReduce where an application is broken down into small parts. Hadoop was named after its chief architect child's stuffed toy elephant.
The current Apache Hadoop ecosystem consists of MapReduce and a number of related projects such as Apache Hive, HBase and Zookeeper. Hadoop is used by companies such as Google, Yahoo and IBM usually for applications such as search engines and advertising.
Hadoop works on Windows and Linux as well as BSD and OS. It stores and processes huge amounts of any kind of data, quickly, with data volumes and varieties increasing from social media and the Internet of Things. Hadoop's distributed computing model processes big data fast, has good fault tolerance; data and application processing are protected against hardware failure and lots of copies of all data are stored automatically.
Hadoop allows you not to preprocess data before storing it. It’s free and uses commonly sold hardware to store large quantities of data. Hadoop allows you to simply grow your system to handle more data simply by adding nodes. Its original focus of being used for searching millions (or billions) of web pages is now turning to many companies using it as their next big data platform. Hadoop's popular uses include being used as a low cost storage and data archive, storing and combining data such as social media, sensor, machine, scientific and click streams. Because it has cheap storage, companies can keep non critical information they may want to analyse later. It can run analytical algorithms.
Big data analytics on Hadoop can help companies operate more efficiently, find new business opportunities and provides competitive advantage, a sandbox approach allows innovation with little cost. Data lakes store data in its original form offering raw or unrefined data to data scientists and analysts and allow them to ask questions without constraints. Data lakes do not replace data warehouses, the question of how to secure and govern data lakes is currently a hot topic, we are now seeing Hadoop sitting beside data warehouse environments and also data sets being put into Hadoop.
Every company wants to store and process data of different forms and to support different uses in the same place. In the Internet of Things, its core is streaming, Hadoop can be used as the data store for billions of transactions, with huge storage and processing capabilities it allows you to use Hadoop as a sandbox.
Instructions can be constantly improved as Hadoop is constantly being updated with new data which doesn’t match previously defined patterns. There are several core modules which are included in the basic framework, Hadoop Common. Hadoop Distributed File System, YARN and MapReduce. Open-source Hadoop software is developed by a network of developers from around the world and it’s free to download, use and contribute to.
More commercial versions of Hadoop are becoming available, these are payable and are versions of the Hadoop framework and have more security, governance, SQL and management/administration console capabilities. Popular distros include Cloudera, Hortonworks, MapR, IBM BigInsights and PivotalHD. There are a few ways of getting data into Hadoop – you can use third-party vendor connectors like SAS/ACCESS or SAS Data Loader for Hadoop. Use Sqoop or Flume to load data into Hadoop.