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IBM

Getting Started with Data Warehousing and BI Analytics

IBM via Coursera

Overview

Data is one of an organization’s most valuable commodities. But how can organizations best use their data? And how does the organization determine which data is the most recent, accurate, and useful for business decision making at the highest level?

After taking this course, you will be able to describe different kinds of repositories including data marts, data lakes, and data reservoirs, and explain their functions and uses.

A data warehouse is a large repository of data that has been cleaned to a consistent quality. Not all data repositories are used in the same way or require the same rigor when choosing what data to store. Data warehouses are designed to enable rapid business decision making through accurate and flexible reporting and data analysis. A data warehouse is one of the most fundamental business intelligence tools in use today, and one that successful Data Engineers must understand.

You will also be able to describe how data warehouses serve a single source of data truth for organization’s current and historical data.

Organizations create data value using analytics and business intelligence applications. Now that you have experienced the ELT process, gain hands-on analytics and business intelligence experience using IBM Cognos and its reporting, dashboard features including visualization capabilities.

Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module.

Syllabus

  • Data Warehouses, Data Marts, and Data Lakes
    • Welcome to your first module! This module provides a gentle but thorough 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 mart, and data lake architecture, and understand how organizations can benefit from each of these three data storage entities. Optionally, you’ll 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. Then, you’ll learn about three types of data warehouse systems and popular data warehouse system vendors. You will be ready to help your organization assess new data warehouse system offerings when you know the five essential, critical criteria, including total cost of ownership, to evaluate before changing to a new data warehouse system.
  • 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 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 learn about populating a data warehouse, incremental data updates, verifying data, querying data, interpreting an entity-relationship diagram for a star schema, creating a materialized view, and applying the CUBE and ROLLUP options. ​You’ll also discover how organizations can benefit by implementing staging.
  • Data Warehouse Analytics
    • In this module, you’ll fast-track your data analytics learning and gain hands-on data analytics experience using IBM Cognos Analytics. After registering with Cognos Analytics, you’ll explore the platform’s capabilities by creating visualizations, building a simple dashboard, and trying out its advanced features.
  • Final Assignment and Final Quiz
    • In this module, you’ll complete your final course project, which brings together concepts and practices you previously learned in the first three modules. In this 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 materialized query tables, known as a materialized view. You will complete your project by using IBM Cognos to create an analytics dashboard.

Taught by

Ramesh Sannareddy and Rav Ahuja

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