Data Science with Hadoop
Benefits of Hadoop
Organisations that are mainly data-driven and process large datasets are increasingly adopting Apache Hadoop as a potential tool. This is because of its ability to process, store and manage large amounts of structured data, unstructured data and semi-structured data.
Increase in the creation and collection of data is often seen as hold-up for Big Data analysis. Many enterprises face the challenges of keeping data on a platform which gives them a constant view. Hadoop cluster provides a highly scalable storage platform. It can store and share datasets across hundreds of low-priced servers.
Hadoop clusters have been determined to be a very cost-effective solution for expanding datasets. Hadoop is an affordable solution as it uses a cluster of commodity hardware to store data. Commodity hardware is cheap machines hence the cost of adding nodes to the framework is not so high.
Hadoop is a distributed process and distributed storage huge amounts of data with high speed. Hadoop even defeats supercomputers, the fastest machine in 2008. It splits up the input data file into a number of blocks and stores data in these blocks over several nodes. It also divides the task that users submit into various sub-tasks which are assigned to these workers containing required data nodes and these sub-task run in parallel for improving the performance.
The built-in failure protection of Hadoop connected with its use of commodity hardware makes it very attractive. It allows the enterprises to store and analyze multiple data types that include images, videos and documents etc. It also makes them simply ready for processing and analysis. This flexibility allows a business to expand and modify their data analysis operations.
- Enhances speed
Hadoop is based on HDFS and MapReduce. HDFS stores data and MapReduce is used for converting data in parallel. The storage method is based on a distributed file system which maps data and then stores it on the cluster. MapReduce us also generally stores the same servers that enhances the faster processing of data.
- Low Network Traffic
In Hadoop, each job submitted by the user is divided into a number of independent sub-tasks and these sub-tasks are authorized to the data nodes thereby moving a small amount of code to data preferably moving huge data to code which leads to low network traffic.