Hadoop Training in Hyderabad

HADOOP/BIG DATA :

Topics covered in this content :

1 .      Introduction to Hadoop / Big Data

2 .      Advantages of Hadoop / Big Data

3 .      Information on Hadoop 2 – latest version of hadoop

4 .      Advantages for Hadoop 2

1.      Introduction to Hadoop / Big Data :

The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell-scripts. Any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program.

Big data/Hadoop is among the most marketed trends in IT field right now, and Hadoop stands front and center in the discussion of how to implement a big data strategy.

Hadoop base framework is composed of the following modules:

·         Hadoop Common – contains libraries and utilities needed by other Hadoop modules;

·         Hadoop Distributed File System (HDFS) – a distributed file-system that stores data providing very high aggregate bandwidth across the cluster;

·         Hadoop YARN – a resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users' applications; and

·         Hadoop MapReduce – a programming model for large scale data processing.

Hadoop splits files into large blocks (default 64MB or 128MB) and distributes the blocks amongst the nodes in the cluster.

From its earliest days, Hadoop has attracted software developers looking to create add-on tools to fill in gaps in its functionality. Other supporting actors that have become Hadoop subprojects or Apache projects in their own right include ; Cassandra, aNOSQL database; that helps in maintaining configuration data and synchronizes distributed operations across clusters.

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2      Advantages of Hadoop/Big Data :

·         Hadoop has the ability to process huge volumes of data in real time.

·         Hadoop's big data processing features are used by people for learning new ways to mine data and discover new relationships in their systems.

·         Hadoop plays major role in big data management and analytics initiatives.

·         Hadoop is very good at analysis of very huge, static unstructured data sets which include  many terabytes or even petabytes of information

·         Because of its ability to handle data "with very light structure, Hadoop applications can take advantage of new information sources that don't lend themselves to traditional databases.

 Hadoop Training in Hyderabad 

3.      Information on Hadoop 2 – latest version of Hadoop :

·         Hadoop’s ongoing evolution :  Hadoop is continually evolving to meet shifting big data management needs and business goals.

·         Hadoop 2 -- originally known as Hadoop 2.0 -- is entering the market. Main in Hadoop2 is YARN, an overhauled resource manager that allows applications other than MapReduce programs to work with HDFS. By doing so, YARN (a good-natured acronym for Yet Another Resource Negotiator) is meant to free Hadoop from its reliance on batch processing while still providing backward compatibility with existing application programming interfaces.

What is YARN ?

YARN is the key difference for Hadoop 2.0 - it allows for multiple workloads to run concurrently.

·         Hadoop 2, will eventually take the framework far beyond its current core configuration.

·         It relates the Hadoop Distributed File System (HDFS) with Java-based MapReduce programs.

·         This pairing is used by the early-adopter companies to help them deal with large amounts of transaction data as well as various types of unstructured and semi-structured data, including server and network log files, sensor data, social media feeds, text documents and image files.

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             Advantages for Hadoop 2 :

·         Hadoop is not useful in real-time analysis of live data sets -- although that could change, thanks to the combination of Hadoop 2 and new query engines recently introduced by some vendors looking to support ad hoc analysis of Hadoop data.

·         Hadoop 2 also shows high availability improvements, through a new feature that allows the users to create federated name (or master) node architecture in HDFS instead of relying on a single node to control an entire cluster. Additionally, it provides support for running Hadoop on Windows. Meanwhile, commercial vendors are brewing up additional management-tool elixirs -- new job schedulers and cluster provisioning software, for example -- in an effort to further boost Hadoop's enterprise readiness.

  Reasons to Learn Hadoop 

1.      Better Career :

2.      Better Salary

3.      Better Job Oppurtunities

4.      Big Companies hiring

Hadoop Training in Hyderabad, Hadoop online training