Hadoop is a software framework that can process large amounts of data (or big data) by dividing it into smaller packets. These small data packets are subsequently processed in multiple computer clusters, and the programming languages used for the purpose are quite simple and based on Java. Hadoop is open-source and uses inexpensive servers, and hence is quite cost efficient. The Hadoop software library can detect the failures and can handle them and hence the framework is fault-resistant. The scalability of Hadoop administration derives from its feature of scaling up to thousands of machines from one single server. The Hadoop architecture is also entirely flexible as its individual components can be swapped and exchanged with the different kinds of software tools. The 2 major components of the Hadoop architecture include:
Author: Priya Jatoliya
The distributed file system (does the job of storing data)The distributed file system or the Hadoop Distributed Filesystem (HDFS) is the component that stores the data that has to be analysed.
The data processing frameworkThe Java application framework is used when you work on the data. It is also known by the name MapReduce. This component actually processes the data.
Modules of HadoopThe Hadoop architecture has modules including:
YARNThe framework does the works towards cluster resource management and job scheduling.
CommonThis module includes the basic Java utilities and modules. The Java files are essential to start the Hadoop framework for the processing of data and performing data analytics. It also includes the OS level abstraction files and the other file systems.
HDFSAs explained earlier this component and module stores the data and enhances the ease of accessing data.
MapReduceThis module and component enable the processing of data at various and multiple computer clusters. With the help of this module, large amounts of data can be processed in a small interval of time. The different Hadoop modules are based on the assumption that the various machines can suffer from the hardware failures and the analytics framework can be used to handle these problems automatically. HDFS and MapReduce are derived from GFS or Google File System and Google MapReduce.
Necessary skills for Hadoop certificationThe Hadoop developers can be differentiated from the software developers in that they work on the big data. Hadoop professional needs to write the programs that match the system design. The professional should be aware of the programming and coding techniques. They should be able to design, create and architect, and also face the issues and problems when they arise. Some of the skills that are necessary towards working on Hadoop are:
- Expertise in Hive and Pig Latin script
- Knowledge in Oozie and other scheduling domains
- Knowledge relating to Flume, Sqoop and other kinds of data loading tools
- Well-versed with Hadoop and its components
- Knowledge about JS, Java, OOAD and Node.js among other programming languages and tools
- Problem identification and solving skills
- Analytical skills
- Knowledge ion HBase
- Well versed with concurrency and multithreading concepts
- Chief data officer
- Big data scientists
- Big data analysis
- Big data engineer
- Big data researcher
Author: Priya Jatoliya