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Bigdata Online Training

Bigdata Online Training:

Hadoop is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become. Why? More data may lead to more accurate analyses.

More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies, cost reductions and reduced risk.

Big data/Hadoop Online Training from india:

As far back as 2001, industry analyst Doug Laney (currently with Gartner) articulated the now mainstream definition of big data as the three Vs of big data: volume, velocity and variety1.

Volume. Many factors contribute to the increase in data volume. Transaction-based data stored through the years. Unstructured data streaming in from social media. Increasing amounts of sensor and machine-to-machine data being collected. In the past, excessive data volume was a storage issue. But with decreasing storage costs, other issues emerge, including how to determine relevance within large data volumes and how to use analytics to create value from relevant data.
Velocity. Data is streaming in at unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time. Reacting quickly enough to deal with data velocity is a challenge for most organizations.
Variety. Data today comes in all types of formats. Structured, numeric data in traditional databases. Information created from line-of-business applications. Unstructured text documents, email, video, audio, stock ticker data and financial transactions. Managing, merging and governing different varieties of data is something many organizations still grapple with.


BigData/Hadoop Course Content:

We provide both Hadoop Development and Admin Training.
What is Hadoop?
History of Hadoop
Building Blocks – Hadoop Eco-System
Who is behind Hadoop?
What Hadoop is good for and why it is Good
Configuring HDFS
Interacting With HDFS
HDFS Permissions and Security
Additional HDFS Tasks
HDFS Overview and Architecture
HDFS Installation
Hadoop File System Shell
File System Java API
Map/Reduce Overview and Architecture
Developing Map/Red Jobs
Input and Output Formats
Job Configuration
Job Submission
Practicing Map Reduce Programs (atleast 10 Map Reduce Algorithms )
4.Getting Started With Eclipse IDE
Configuring Hadoop API on Eclipse IDE
Connecting Eclipse IDE to HDFS
5.Hadoop Streaming
6.Advanced MapReduce Features
Custom Data Types
Input Formats
Output Formats
Partitioning Data
Reporting Custom Metrics
Distributing Auxiliary Job Data
7.Distributing Debug Scripts
8.Using Yahoo Web Services
Pig Overview
Pig Latin
Pig with HDFS
10. Hive
Hive Overview
Hive QL
Hive Unstructured Data Analyzation
Hive Semistructured Data Analyzation
HBase Overview and Architecture
HBase Installation
HBase Shell
CRUD operations
Scanning and Batching
HBase Key Design
Zoo Keeper Overview
Server Mantainace
Sqoop Overview
Imports and Exports
Basic Setup
Important Directories
Selecting Machines
Cluster Configurations
Small Clusters: 2-10 Nodes
Medium Clusters: 10-40 Nodes
Large Clusters: Multiple Racks
16.Putting it all together
Distributed installations
Best Practices

And Many more concepts……………………………..


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