SKILLSHARE Data Warehouse Development Process - Part 2-iLLiTERATE

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SKILLSHARE DATA WAREHOUSE DEVELOPMENT PROCESS PART 2
English | Size: 365.13 MB
Category: Tutorial

What Will I Learn?

Understand various stages in Data Warehouse development process
Various processes like Waterfall model, V model and Agile methods
Specific aspects of Data Warehouse development process
Importance of the various phases and the practicality of each phase
Overview of various issues and Project Management issues to be considered in the Data warehouse and Business Intelligence projects

SKILLSHARE DATA WAREHOUSE DEVELOPMENT PROCESS PART 2
English | Size: 365.13 MB
Category: Tutorial

What Will I Learn?

Understand various stages in Data Warehouse development process
Various processes like Waterfall model, V model and Agile methods
Specific aspects of Data Warehouse development process
Importance of the various phases and the practicality of each phase
Overview of various issues and Project Management issues to be considered in the Data warehouse and Business Intelligence projects
Requirements:

Basics of Data Warehouse Concepts
Terms and terminologies used in a Data Warehouse and Business Intelligence projects
Description:

Data is the new asset for the enterprises. And, Data Warehouse store the data for better insights and knowledge using Business Intelligence. Development of an Enterprise Data Warehouse has more challenges compared to any other software projects because of the

Challenges with data structures
The way data is evaluated for it's quality
Complex business rules/validations
Different development methods (various SDLC models like Water Fall model, V model, Agile Model, Incremental model, Iterative model)
Regulatory requirements for various domains like finance, telecom, insurance, Retail and IME
Compliance from third party governing bodies
Extracting data for various visualization purposes
In this course, we talk about the specific aspects of the Data Warehouse Development process taking real time practical situations and how to handle them better using best practices for sustainable, scalable and robust implementations.

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