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Big Data isn’t new. Earlier the ability of understanding the data was limited because of the unavailability of tools for system support.
Hadoop was mainly built up to manage Big Data plan. Open source frameworks for reliable and scalable distributed computing Hadoop can efficiently store and process large datasets using commodity hardware.
Components of HADOOP
In order to search it effectively with a huge amount of generated data every minute, Google needed to be indexed, but maintaining that index was extremely challenging. With the development of Hadoop, maintaining this much amount of data has become easier.
Hadoop is made up of four modules; the two key modules provide storage and processing functions.
Hadoop can even run on a single machine, but normally Hadoop runs in a cluster configuration. Clusters can range from just a few nodes to thousands of nodes.
Benefits of Using HADOOP
For solving big data applications HADOOP provides number of benefits:-
Cost Effective: - Hadoop is much cost effective than ordinary commodity hardware which requires large capacity storage and high availability.
Effective working: A the movement of data in Hadoop is not in between servers , the volume does not overload the system and the problem is efficiently sorted out due to the use of multiple nodes on the problem parts.
Extensible: In Hadoop servers can be added dynamically, which in turns increases the storage and speed.
Flexibility: Although most commonly used to run MapReduce, it can be used to run other applications, as well. It can handle any type of data, structured or unstructured.
These advantages and adaptability don't imply that Hadoop is appropriate for each problem. Issues with smaller data can be easily solved.
Hadoop also may be a secondary choice for storing highly sensitive data. Security with Hadoop is considered to be the main concern as the default configurations disables it.
Hadoop is cost effective simply for data storage. It can be effectively be utilized as an organizing area before stacking information into a data center.
Businesses that have connected Hadoop to their Big Data issues in the previous couple of years includes retail, saving money, human services, and numerous others. The cost effectiveness comes from utilization of product equipment. The substantial information sets are basically separated and put away on common measured local disks. Software handles any kind of failures rather than putting up high cost servers.
In future understanding Hadoop will be necessary for organizations that want to start getting value out of their databases, data warehouses, and data lakes.