How Big Data Hadoop Works?

Hadoop is formally known as Apache Hadoop. It is an open source framework developed within the Apache Software Foundation. Hadoop’s framework is used for storing data and running applications on clusters of the commodity.

The architecture of Apache Hadoop framework consists of Hadoop Distributed File System (HDFS) which is used for storing data on commodity machines, MapReduce programming model which is used for processing, Hadoop Common which is used to store libraries and utilities for the use of other Hadoop modules and Hadoop YARN which is a resource management platform and is used for scheduling user’s applications and managing resources in clusters. Hadoop works on the divide and solves policy as it divides files into large blocks and disperses them into nodes of clusters, then packaged codes are sent to the clusters to process the data in parallel. This approach ensures the fast and efficient processing of dataset as compared to conventional supercomputer architecture. A few drawbacks of Apache Hadoop are that MapReduce programming is not a good match for all the problems, data security issues and does not have full-featured tools for data management.

The term big data refers to enormous and complex data sets that are hard to process by traditional data processing application software. In the 1990's, even one terabyte was considered as big data and to store it, the data warehouses were created. Characteristics of Big data are Volume i.e. the quantity of generated and stored data; Variety i.e. the type and nature of the generated and stored data; Velocity i.e. the speed at which data is generated and processed and Veracity i.e. the quality of generated and stored data. The challenges that are faced while dealing with big data includes visualization, data sharing, data search, data transfer, capturing data, data analysis, data storage, data updating, data source, querying and information privacy.

Whenever someone is talking about Big Data management or analytics, Hadoop is always mentioned as Hadoop is considered the best way to process a huge amount of data faster and efficiently. Hadoop puts right Big Data workloads in systems and optimizes data structure in an organization. Apache Hadoop is majorly considered by organizations to process and manage Big Data because of its cost-effectiveness, systematic and scalability architecture. Lately, firms are realizing that analyzing and categorizing Big Data helps in making business predictions. Big Data Hadoop works by using MapReduce programming model of Apache Hadoop as it is used for processing different types of data.

Various Big Data tools that has been built around Apache Hadoop to extend its basic capabilities and to increase the efficiency of data analysis includes Apache ZooKeeper which is a synchronization, naming registry and configuration service for distributed systems, Apache Pig which is a high level platform for creating programs, Apache HBase which is distributed database that is paired with Hadoop, Apache Oozie which is a server-based workflow scheduling system to manage Hadoop jobs, Apache Sqoop tool helps in transferring bulk data between Hadoop and relational databases, Apache Phoenix is an SQL based parallel processing database engine which uses HBase as its data store and Apache Hive which is an SQL on Hadoop tool that provides data query, data summarization and data analysis.

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