Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation.
Hadoop makes it possible to run applications on systems with thousands of commodity hardware nodes, and to handle thousands of terabytes of data. Its distributed file systemfacilitates rapid data transfer rates among nodes and allows the system to continue operating in case of a node failure. This approach lowers the risk of catastrophic system failure and unexpected data loss, even if a significant number of nodes become inoperative. Consequently, Hadoop quickly emerged as a foundation for big dataprocessing tasks, such as scientific analytics, business and sales planning, and processing enormous volumes of sensor data, including from internet of things sensors.
Hadoop was created by computer scientists Doug Cutting and Mike Cafarella in 2006 to support distribution for the Nutch search engine. It was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts, which are also called fragments or blocks, can be run on any node in the cluster. After years of development within the open source community, Hadoop 1.0 became publically available in November 2012 as part of the Apache project sponsored by the Apache Software Foundation.
What are the objectives of our Big Data Hadoop Online Course?
The Big Data Hadoop Certification course is designed to give you in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion with our big data training.
You will master real-time data processing using Spark, including functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimization techniques. With our big data course, you will also learn the various interactive algorithms in Spark and use Spark SQL for creating, transforming,and querying data forms.
As a part of the big data course, you will be required to execute real-life industry-based projects using CloudLab in the domains of banking, telecommunication, social media, insurance, and e-commerce. This Big Data Hadoop training course will prepare you for the Cloudera CCA175 big data certification.
Big data comprises of large volume of datasets which is very difficult to manage within the traditional computer. The big data includes huge volume, high velocity and extended variety of data.
Hadoop is an open source framework written in java which is used to manage the large volume of datasets by the clusters of computers using the mapreduce concept. Hadoop MapReduce is a software framework in which the map collects the large input of data and converts into the sets of data whereas the reduction of these datasets are performed after the map process. The most common file system used in the hadoop is Hadoop Distributed File System (HDFS) and it follows the master/slave technique. HBase is the column based distributed database management system in which the data is stored in the form of columns in the tables whereas the traditional RDBMS stores the data in the form of rows. An unique opportunity to gain hands-on experience in Hadoop.
This course provides the Best Hadoop training for Graduate and Post Graduate Engineering students and Research scholars specialized in Hadoop (B.E /B.Tech /M.E /M.Tech /M.S /Ph.D). Syllabus can be customized according to requirements of the student.
Introduction to Bigdata and Hadoop
• Installation of single & multinode cluster in Hadoop and HBase
• HDFS (Hadoop Distributed File System) Features & Commands
• Five daemons of Hadoop
• Architecture and Working of Mapreduce & HBase
• Programming structure of Hadoop & HBase
• Sample programs in Mapreduce
• HBase shell Commands
• Seating is limited, and pre-registration is required.
• Weekdays and weekend classes
• Unlimited Practical hours
• Free software installation
• Lot of samples for Mapreduce
• Training Certificate
• Softcopy of the materials