IBM
Getting Started with Data Warehousing and BI Analytics
IBM

Getting Started with Data Warehousing and BI Analytics

This course is part of multiple programs.

Taught in English

Some content may not be translated

Ramesh Sannareddy
Rav Ahuja

Instructors: Ramesh Sannareddy

23,333 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.4

(154 reviews)

|

86%

Intermediate level

Recommended experience

16 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Explore the architecture, features, and benefits of data warehouses, data marts, and data lakes and identify popular data warehouse system vendors.

  • Design and populate a data warehouse, and model and query data using CUBE, ROLLUP, and materialized views.

  • Identify popular data analytics and business intelligence tools and vendors and create data visualizations using IBM Cognos Analytics.

  • Design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.

Details to know

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Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(154 reviews)

|

86%

Intermediate level

Recommended experience

16 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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There are 4 modules in this course

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.

What's included

7 videos2 readings2 quizzes1 app item2 plugins

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.

What's included

8 videos3 readings2 quizzes6 app items1 plugin

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.

What's included

6 videos1 reading2 quizzes5 plugins

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.

What's included

3 readings1 quiz1 peer review2 app items1 plugin

Instructors

Instructor ratings
4.4 (29 ratings)
Ramesh Sannareddy
IBM
12 Courses266,233 learners

Offered by

IBM

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