Big Data Engineering for Analytics

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