Best hadoop online training in India


Hadoop MapReduce is a software framework for easily writing applications which process big amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

The term MapReduce actually refers to the following two different tasks that Hadoop programs perform:

  • The Map Task: This is the first task, which takes input data and converts it into a set of data, where individual elements are broken down into tuples (key/value pairs).
  • The Reduce Task: This task takes the output from a map task as input and combines those data tuples into a smaller set of tuples. The reduce task is always performed after the map task.

Typically both the input and the output are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster-node. The master is responsible for resource management, tracking resource consumption/availability and scheduling the jobs component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves TaskTracker execute the tasks as directed by the master and provide task-status information to the master periodically.

The JobTracker is a single point of failure for the Hadoop MapReduce service which means if JobTracker goes down, all running jobs are halted.


Big Data Hadoop Certification Training in Hyderabad, India

What skills will you learn with our Big Data Hadoop Certification Training?

Big Data Hadoop training in Hyderabad will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. You will learn to:

Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark with this Hadoop course.

  • Understand Hadoop Distributed File System (HDFS) and YARN architecture, and learn how to work with them for storage and resource management
  • Understand MapReduce and its characteristics and assimilate advanced MapReduce concepts
  • Ingest data using Sqoop and Flume
  • Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
  • Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
  • Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
  • Understand and work with HBase, its architecture and data storage, and learn the difference between HBase and RDBMS
  • Gain a working knowledge of Pig and its components
  • Do functional programming in Spark, and implement and build Spark applications
  • Understand resilient distribution datasets (RDD) in detail
  • Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
  • Understand the common use cases of Spark and various interactive algorithms
  • Learn Spark SQL, creating, transforming, and querying data frames
  • Prepare for Cloudera CCA175 Big Data certification


Hadoop Online Training In India

Big Data Hadoop Certification Training : It is a comprehensive Hadoop Big Data training course designed by industry experts considering current industry job requirements to provide in-depth learning on big data and Hadoop Modules. This is an industry recognized Big Data certification training course that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing, and analytics. This Cloudera Hadoop training will prepare you to clear big data certification.

Hadoop Online Training

Apache Hadoop is a collection of open-source software utilities that facilitate using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Originally designed for computer clusters built from commodity hardware—still the common use—it has also found use on clusters of higher-end hardware.All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework