TrainersGlobe - Bigdata Trainings, Certification, Interview, Job Support
Bigdata Training Course
Prism IT Corp 2017 H1B Sponsorship
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  • Bigdata Course Content
    • Note : We Provide Customized Course Content Based On Your Requirement.

      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