Big Data Training

Big Data Online Training Course Content:

Introduction to Big Data

Defining Big Data

» The four dimensions of Big Data: volume, velocity, variety, veracity
» Introducing the Storage, MapReduce and Query Stack

Delivering business benefit from Big Data

» Establishing the business importance of Big Data
» Addressing the challenge of extracting useful data
» Integrating Big Data with traditional data

Storing Big Data

Analyzing your data characteristics

» Selecting data sources for analysis
» Eliminating redundant data
» Establishing the role of NoSQL

Overview of Big Data stores

» Data models: key value, graph, document, column-family
» Hadoop Distributed File System
» HBase
» Hive
» Cassandra
» Hypertable
» Amazon S3
» BigTable
» DynamoDB
» MongoDB
» Redis
» Riak
» Neo4J

Selecting Big Data stores

» Choosing the correct data stores based on your data characteristics
» Moving code to data
» Implementing polyglot data store solutions
» Aligning business goals to the appropriate data store

Processing Big Data

Integrating disparate data stores

» Mapping data to the programming framework
» Connecting and extracting data from storage
» Transforming data for processing
» Subdividing data in preparation for Hadoop MapReduce

Employing Hadoop MapReduce

» Creating the components of Hadoop MapReduce jobs
» Distributing data processing across server farms
» Executing Hadoop MapReduce jobs
» Monitoring the progress of job flows

The building blocks of Hadoop MapReduce

» Distinguishing Hadoop daemons
» Investigating the Hadoop Distributed File System
» Selecting appropriate execution modes: local, pseudo-distributed, fully distributed

Tools and Techniques to Analyze Big Data

Abstracting Hadoop MapReduce jobs with Pig

» Communicating with Hadoop in Pig Latin
» Executing commands using the Grunt Shell
» Streamlining high-level processing

Performing ad-hoc Big Data querying with Hive

» Persisting data in the Hive MegaStore
» Performing queries with HiveQL
» Investigating Hive file formats

Creating business value from extracted data

» Mining data with Mahout
» Visualizing processed results with reporting tools

Developing a Big Data Strategy

Defining a Big Data strategy for your organization

» Establishing your Big Data needs
» Meeting business goals with timely data
» Evaluating commercial Big Data tools
» Managing organizational expectations

Enabling analytic innovation

» Focusing on business importance
» Framing the problem
» Selecting the correct tools
» Achieving timely results

Statistical analysis of Big Data

» Leveraging RHadoop functionality
» Generating statistical reports with RHadoop
» Exploiting RHadoop visualization
» Making use of analytical results

Implementing a Big Data Solution

» Selecting suitable vendors and hosting options
» Balancing costs against business value
» Keeping ahead of the curve

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