Big Data Introduction Training Course

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

Analysing 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 Analyse 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
    Visualising processed results with reporting tools
    Querying in real time with Impala

Developing a Big Data Strategy

Defining a Big Data strategy for your organisation

    Establishing your Big Data needs
    Meeting business goals with timely data
    Evaluating commercial Big Data tools
    Managing organisational 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