Introduction to Big Data Training

Course Outline

• 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