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
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