In this Introduction to Big Data training course you will learn ways of storing data that allow for efficient processing and analysis, and 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.
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
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
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
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
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
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
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
Selecting suitable vendors and hosting options
Balancing costs against business value
Keeping ahead of the curve
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19/01/2018: Having established itself as a key part of corporate Big Data programs, Hadoop continues to grow in importance. Unsurprisingly, Hadoop and Big...
16/01/2018: As Big Data becomes an integral part of the data-driven enterprise, businesses are encountering problems securing the skills they need to make...
14/01/2018: Python, as we all know, is a general-purpose programming language that is fast becoming more and more popular for doing data science. Companies...
16/01/2018: Data Analysts at a Government establishment required the ability to respond rapidly to ad-hoc requests for information, including parliamentary...
18/12/2017: The Client A leading developer and manufacturer of sophisticated industrial products, abatement solutions and related value-added services. Their...
13/10/2017: This organisation needed their Supply Chain department to get fully involved with Microsoft’s Power BI reporting product as soon as possible....
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