Big Data Introduction training course

Leverage big data analysis tools and techniques to foster better business decision-making

  • Train a team
  • Free tasters
  • £1,600.00+VAT
  • Next on 27 Sep

JBI training course London UK

  • Gain an introduction to Big Data
  • Learn how to define Big Data
  • Select the correct Big Data stores for disparate data sets
  • Process large data sets using Hadoop to extract value
  • Store, manage and analyse unstructured data
  • Leverage Big Data analysis tools and techniques to foster better business decision-making
  • Query large data sets in near real-time with Pig and Hive
  • Plan and implement a Big Data strategy for your organisation


In this Introduction to Big Data training course, you will learn ways of storing data that allow for efficient processing and analysis. You will also gain the skills you need to store, manage, process and analyse massive amounts of unstructured data to create an appropriate data lake.

Get introduced to leading products such as Hadoop to learn how to apply Big Data in the real world.

JBI training course London UK
JBI training course London UK

IT professionals looking to learn about how to implement and  enhance a corporate big data environment and looking to get a better elementary practical skills relating to Big Data


Related Courses

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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