Big Data Training

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Big Data Training


 (4.9) | 350 Ratings


Introduction


Big Data Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 35 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

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 and fully distributed

Handling streaming data

  • Comparing real–time processing models
  • Leveraging Storm to extract live events
  • Lightning–fast processing with Spark and Shark

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
  • Querying in real time with Impala

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

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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Course Curriculum

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 and fully distributed

Handling streaming data

  • Comparing real–time processing models
  • Leveraging Storm to extract live events
  • Lightning–fast processing with Spark and Shark

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
  • Querying in real time with Impala

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

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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