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Description

Master Data Warehousing, Dimensional Modeling & ETL process
Do you want to learn how to
implement a data warehouse in a modern way?
This is
the only course
you need to
master architecting and implementing a data warehouse
end-to-end!
Data Modeling and data warehousing is one of the
most important skills in Business Intelligence & Data Engineering!
This is the
most comprehensive & most modern course
you can find on data warehousing.
Here is why:
Most comprehenisve course with 9 hours video lectures
Learn from a real expert - crystal clear & straight-forward
Master theory & practice - hands-on demonstrations, assignments & quizzes
We will implement a complete data warehouse - end-to-end
Understand everything step by step from the absolute basics to the advanced topics
Learn the practical steps and the important theory to upskill your career
This course will take you all the way to being able to architect and implement a data warehouse in a company in a professional manner.
Here is what you'll learn:
Data Warehouse Basics
Data Warehouse architecture
Data Warehouse infrastructure
Data Modeling
Setting up an ETL process
Dimensional Modeling: Facts & Dimensions
Implementing a comeplete data warehouse hands-on
Slowly Changing Dimensions
Understanding ETL tools
ELT vs. ETL
Advanced topics like: Columnar storage, OLAP Cubes, In-memory databases, massive parallel processing & cloud data warehouses
Optimizing a data warehouse using indexes (B-tree indexes & Bitmap indexes)
Practically using and connecting a data warehouse
By the end of this course you will be able to design & build a complete data warehouse from the ground up. You will have the knowledge, the practical skills and the confidence to implement a modern data warehouse professionally.
Everything you need to be a highly proficient data architect, data engineer, data analyst or Business Intelligence expert!
Join now to get instant & lifetime access - of course backed by the no-questions-asked 30 days money back guarantee!
Who this course is for:
Data Analyst that want to upskill and learn how to build a data warehouse
Data Engineers that want to learn about data warehousing and data modeling
People that want to become a data architect, BI consultant, data engineer or data analyst
Data professionals that want to upskill in Business Intelligence & Data Modeling

What you'll learn

Architect & implement a professional data warehouse end-to-end

You will learn the principles of Data Warehouse Design

You will master ETL process in both theory & practise

You will implement in a case study your own data warehouse & ETL process

You will learn the modern architecture of a Data Warehouse

Dimensional Modeling in a professional way

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Intro
4
1.1-Welcome!
1.2-How this course works
1.3-What do you learn in this course?
1.4-Course slides
2-Data Warehouse Basics
10
2.1-Why a data warehouse?
2.2-What is a data warehouse?
2.3-The purpose of a data warehouse
2.4-What is Business Intelligence?
2.5-Data Lake or Data Warehouse?
2.6-Data Lake & Business Intelligence
2.7-Setting up Pentaho (ETL tool)
2.8-Demos & Hands-on
2.9-Pentaho for Mac M1/M2: Installation & Troubleshooting
2.10-Setting up PostgreSQL (Database system)
3-Data Warehouse Architecture
11
3.1-3 Layers of a Data Warehouse
3.2-Staging area
3.3-Demo: Setting up the staging area
3.4-Data Marts
3.5-Staging area & Data Marts
3.6-Relational databases
3.7-In-Memory databases
3.8-Cubes
3.9-Different database systems
3.10-Operational Data Storage
3.11-Summary
4-Dimensional Modeling
10
4.1-What is dimensional modeling?
4.2-Why dimensional modeling?
4.3-Facts
4.4-Dimensions
4.5-Star schema
4.6-Snowflake schema
4.7-Demo: Product & Category dimension (snowflaked)
4.8-Facts & Dimensions (ProjectLogs)
4.9-Facts & Dimensions (SalesTransactions)
4.10-Dimensional modeling
5-Facts
18
5.1-Additivity
5.2-Nulls in facts
5.3-Year-to-Date facts
5.4-Facts I
5.5-Types of fact tables
5.6-Transactional fact tables
5.7-Periodic fact tables
5.8-Accumulating snapshots
5.9-Comparing fact table types
5.10-Identifying fact tables
5.11-Factless fact tables
5.12-Steps in designing fact tables
5.13-Surrogate Keys
5.14-Case Study: The Project
5.15-Case Study: Identify the business process
5.16-Case Study: Define the grain
5.17-Case Study: Identify the dimensions
5.18-Case Study: Identify the facts
6-Dimensions
10
6.1-Dimension tables
6.2-Date dimensions
6.3-Nulls in dimensions
6.4-Hierarchies in dimensions
6.5-Conformed dimensions
6.6-Degenerate dimensions
6.7-Junk dimension
6.8-Role-playing dimension
6.9-Case Study: Date dimension
6.10-Understanding dimensions
7-Slowly Changing Dimensions
8
7.1-What are slowly changing dimensions?
7.2-Type 0 - Original
7.3-Type 1 - Overwrite
7.4-Type 2 - Additional row
7.5-Administrating Type 2 dimensions
7.6-Mixing Type 1 & Type 2
7.7-Type 3 - Additional attribute
7.8-Understanding Slowly Changing Dimensions
8-ETL process
17
8.1-Understanding the ETL process
8.2-Extract
8.3-Initial Load
8.4-Delta Load
8.5-Load Workflow
8.6-Demo: Quick Intro to Pentaho
8.7-Demo: Setting up tables in SQL
8.8-Demo: Initial Load example
8.9-Demo: Delta Load example
8.10-Transforming data
8.11-Basic Transformations
8.12-Advanced Transformations
8.13-Demo: Planning next steps
8.14-Demo: Table setup & Complete Staging
8.15-Demo: Transform
8.16-Demo: Load & Validate results
8.17-Scheduling jobs
9-ETL tools
2
9.1-ETL tools
9.2-Choosing the right ETL tool
10-Case Study: Creating a Data Warehouse
9
10.1-Plan of attack
10.2-Source data & table design
10.3-Setting up the tables in database
10.4-Staging: Sales Fact
10.5-Staging job & fixing problems
10.6-Load Payment Dimension
10.7-Transform & Load Sales Fact
10.8-Transform & Load job
10.9-Final ETL job & Incremental Load
11-ETL vs. ELT
2
11.1-What is an ELT?
11.2-ETL vs. ETL
12-Using a Data Warehouse
2
12.1-What are the common use cases?
12.2-Connecting the DWH to Power BI
13-Optimizing a Data Warehouse
5
13.1-Using indexes
13.2-B-tree indexes
13.3-Bitmap indexes
13.4-Guidelines for indexes
13.5-Demo: Setting indexes
14-The Modern Data Warehouses
4
14.1-Cloud vs. on-premise
14.2-Benefits cloud vs on-premise
14.3-Massive parallel processing
14.4-Columnar storage
15-Bonus
1
15.1-Bonus lecture