https://github.com/kmohamedalie/data-warehouse-ibm

Data Warehouse IBM 🔡🔢🏭
https://github.com/kmohamedalie/data-warehouse-ibm

business-intelligence coursera dataengineering datawarehousing ibm

Last synced: 6 days ago
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Data Warehouse IBM 🔡🔢🏭

• Host: GitHub
• URL: https://github.com/kmohamedalie/data-warehouse-ibm
• Owner: Kmohamedalie
• Created: 2024-07-07T10:43:12.000Z (4 months ago)
• Default Branch: master
• Last Pushed: 2024-07-09T01:14:47.000Z (4 months ago)
• Last Synced: 2024-07-09T12:27:20.642Z (4 months ago)
• Topics: business-intelligence, coursera, dataengineering, datawarehousing, ibm
• Language: Python
• Homepage:
• Size: 2.96 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# [Data Warehouse Fundamental IBM](https://www.coursera.org/learn/data-warehouse-fundamentals/home/welcome)

### **Module 1 :  Data Warehouses, Data Marts, and Data Lakes**

This module provides an introduction to data warehouse systems, data lakes, and data marts. When you complete this module, you’ll be able to identify and compare data warehouse systems, data marts, and data lakes based on their architecture, and understand how organizations can benefit from each of these three data storage entities. Then, you’ll learn about three types of data warehouse systems and popular data warehouse system vendors. You will learn to help your organization assess new data warehouse system offerings when you know the five essential, critical criteria, including the total cost of ownership, to evaluate before changing to a new data warehouse system.

### **Module 2 : Designing, Modeling, and Implementing Data Warehouses**

In this knowledge-packed module, you’ll explore general and reference enterprise data warehousing architecture. You’ll discover how data cubes relate to star schemas. Then, you’ll learn how to slice, dice, drill up or down, roll up, and pivot relative to data cubes. Next, you will examine the capabilities of materialized views, their benefits, and how to apply them. You’ll learn how a data organization using facts and dimensions and their related tables organizes information. Then, you will explore how to use normalization to create a snowflake schema as an extension of the star schema. You will also learn about populating a data warehouse, incremental data updates, verifying data, querying data, and interpreting an entity-relationship diagram for a star schema. Finally, the module will delve into the creation of a materialized view, the application of cube and rollup options, and examine the advantages organizations gain from implementing staging.

### **Module 3 : Final Assignment and Final Quiz**

In this module, you’ll complete your practice project and final course project, which bring together concepts and practices you previously learned in the first two modules. In the final project, you will design and load data into a data warehouse using facts and dimension tables. Then you’ll write aggregation queries using cube and rollup functions and create a materialized view. In the optional lesson, you will explore the workings of IBM Db2 data warehouse system architecture, view use cases, and understand the key capabilities and integrations available with IBM Db2 Warehouse. The hands-on labs in this lesson will enable you to gain practical knowledge on how to create a Db2 service instance, how to populate a data warehouse using IBM Db2, how to query the data warehouse using IBM Db2.