Big Data Engineering for Analytics

Created by LEC Team
Last updated Mon, 27-Mar-2023
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Course Objectives of Big Data Engineering for Analytics

  • Understand the fundamental characteristics, storage, analysis techniques, and the relevant distributions

  • Gain expertise with the fault-tolerant computing framework 

  • Construct configurable and executable tasks 

  • Understand the nuances of writing functional programs 

  • Understand various data processing, querying, and persistence available for usage in RDD’s context. 

  • Perform tasks such as filtering, selection, and categorization.



Day 1
Data Science, Data Engineering, Big Data, and Analytics Perspective

  • Introduction to Data Science, Data Engineering, and Big Data

  • Data Scientist vs. Data Engineer

  • Different Roles in Data Engineering

  • Core Data Engineering Skills and Resources

  • Understand Big Data from an Analytics Perspective



Day 2
Architectural Viewpoints and Hadoop Ecosystem

  • Architectural Viewpoints in Big Data

  • Reference Architecture Conceptual View

  • Reference Architecture Logical View

  • Oracle Product Mapping View

  • The Hadoop Ecosystem for Big Data



Day 3
File Storage and Databases for Big Data

  • Distributed File Storage

  • NoSQL Databases for Big Data

  • Spark and Functional Programming for Big Data



Day 4
Management of Big Data

  • Spark and Resilient Distributed Data Sets

  • Spark QL for Big Data

  • Spark and Real-Time Stream Processing

  • Management of Big Data initiatives



Day 5
Dealing with a case study
  • Case study

  • Project Requirement Elaboration

  • Project and Assessment

  • Project Demonstration

  • Report Submission and Presentations


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