Big data is a collection of large data sets produced by various devices and applications with the use of different tools, techniques and framework. It is one of the emerging technology that deals with consumers, product companies and retail marketing. Big data also caused for the revolutionary changes in medical field, by storing the data of previous medical history and providing better and quick services. The decision making strategy is resulting in better operational efficiency, cost reductions and risk reducing factors for businesses. Big data systems are designed with new cloud computing architecture that allows computations to run efficiently. This feature actually reduces the work overload of operational big data and enables faster implementations. There are some special file systems that minimize the coding burden with the help of real time data without the need of data scientists and additional infrastructure.
Hadoop is an open source framework that stores and process big data across the clusters of computers with the help of simple programming models. Hadoop includes the features of flexibility, fault tolerance, cost effectiveness, good computing power and scalability. These features are the important reason that organisations turn to Hadoop and its capability of storing huge data. Hadoop comes up as next big data platform due its different uses and that are bellows:
Low data storage
Data warehouse and analytics store
Sandbox for analysis
How to get data into Hadoop?
There are some important ways to get the required data by following below mentioned activities:
- Loading the files into system by using Java commands. Multiple files copies across multiple nodes are taken care by HDFS.
- Shell scripts runs multiple “put” command parallel, for large number of files.
- Copy or write files by mounting HDFS as file system.
- Use of third party vendor.
Hadoop Analytical Approach
Hadoop data storage and synchronization are allowing programmers to focus on coding for better analyzation of data.
Ambari: It is a web interface for managing, configuring and testing the Hadoop services. It offers web based GUI with wizard scripts for setting most of the standard components.
Pig: It is a platform for manipulating the data that are stored in HDFS file system. This includes compiler for MapReduce programs that provide a way to perform data extraction, transformation and loading.
Hive: It is an SQL like query language and a data warehousing that presents data in the form of tables. This programming concept is similar to database programming.
HBase: Whenever there is data fall in big tables, then HBase will search it and store it. It automatically shares the table across multiple nodes, so that MapReduce can run locally to serve input and output jobs.
HCatalog: It is a storage management layer that helps user to share and access data.
Sqoop: Sqoop moves large tables that are full of information, to other tools like Hive or HBase.
Spark: It is an open source cluster computing framework that lies within in-memory analytics.
Zookeeper: It stores all the metadata for machines that can synchronize the work of various machines. It coordinates with distributed application processes.