Introduction to Big Data
About Individual Course:
Introduction to Big Data is an intermediate level, Data Science training based course that allows students to learn how to leverage big data analysis tools and techniques to facilitate a better business decision-making. Furthermore, students also acquire hands-on knowledge on storing data in order to regulate efficient processing and analysis, and acquire the expertise to store, manage, process, and analyze large amounts of unstructured data and develop a relevant data lake.
Store, manage, and analyze the unstructured data sets
Choose the right big data stores covering disparate data sets
Process large data sets through Hadoop to acquire value
Query large data sets in almost real time through Pig and Hive
Craft and execute a big data strategy for a business
A sound expertise of the Microsoft Windows platform
Virtual Instructed-Led Outline
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
- Amazon S3
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
|Learning Style||Virtual Classroom|
|Course Duration||3 Days|
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