Learn Data Warehousing in 24 Hours (eBook, ePUB)
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Unlike popular belief, Data Warehouse is not a single tool but a collection of software tools. A data warehouse will collect data from diverse sources into a single database. Using Business Intelligence tools, meaningful insights are drawn from this data.The best thing about "Learn Data Warehousing in 1 Day" is that it is small and can be completed in a day. With this e-book, you will be enough knowledge to contribute and participate in a Data warehouse implementation project.The book covers upcoming and promising technologies like Data Lakes, Data Mart, ELT (Extract Load Transform) amongst ot...
Unlike popular belief, Data Warehouse is not a single tool but a collection of software tools. A data warehouse will collect data from diverse sources into a single database. Using Business Intelligence tools, meaningful insights are drawn from this data.
The best thing about "Learn Data Warehousing in 1 Day" is that it is small and can be completed in a day. With this e-book, you will be enough knowledge to contribute and participate in a Data warehouse implementation project.
The book covers upcoming and promising technologies like Data Lakes, Data Mart, ELT (Extract Load Transform) amongst others. Following are detailed topics included in the book
Table content
Chapter 1: What Is Data Warehouse?
What is Data Warehouse?
Types of Data Warehouse
Who needs Data warehouse?
Why We Need Data Warehouse?
Data Warehouse Tools
Chapter 2: Data Warehouse Architecture
Characteristics of Data warehouse
Data Warehouse Architectures
Datawarehouse Components
Query Tools
Chapter 3: ETL Process
What is ETL?
Why do you need ETL?
ETL Process
ETL tools
Chapter 4: ETL Vs ELT
What is ETL?
Difference between ETL vs. ELT
Chapter 5: Data Modeling
What is Data Modelling?
Types of Data Models
Characteristics of a physical data model
Chapter 6: OLAP
What is Online Analytical Processing?
Types of OLAP systems
Advantages and Disadvantages of OLAP
Chapter 7: Multidimensional Olap (MOLAP)
What is MOLAP?
MOLAP Architecture
MOLAP Tools
Chapter 8: OLAP Vs OLTP
What is the meaning of OLAP?
What is the meaning of OLTP?
Difference between OLTP and OLAP
Chapter 9: Dimensional Modeling
What is Dimensional Model?
Elements of Dimensional Data Model
Attributes
Difference between Dimension table vs. Fact table
Steps of Dimensional Modelling
Rules for Dimensional Modelling
Chapter 10: Star and SnowFlake Schema
What is Multidimensional schemas?
What is a Star Schema?
What is a Snowflake Schema?
Difference between Start Schema and Snowflake
Chapter 11: Data Mart
What is Data Mart?
Type of Data Mart
Steps in Implementing a Datamart
Chapter 12: Data Mart Vs Data Warehouse
What is Data Warehouse?
What is Data Mart?
Differences between a Data Warehouse and a Data Mart
Chapter 13: Data Lake
What is Data Lake?
Data Lake Architecture
Key Data Lake Concepts
Maturity stages of Data Lake
Chapter 14: Data Lake Vs Data Warehouse
What is Data Warehouse?
What is Data Lake?
Key Difference between the Data Lake and Data Warehouse
Chapter 15: What Is Business Intelligence?
What is Business Intelligence
Why is BI important?
How Business Intelligence systems are implemented?
Four types of BI users
Chapter 16: Data Mining
What is Data Mining?
Types of Data
Data Mining Process
Modelling
The best thing about "Learn Data Warehousing in 1 Day" is that it is small and can be completed in a day. With this e-book, you will be enough knowledge to contribute and participate in a Data warehouse implementation project.
The book covers upcoming and promising technologies like Data Lakes, Data Mart, ELT (Extract Load Transform) amongst others. Following are detailed topics included in the book
Table content
Chapter 1: What Is Data Warehouse?
What is Data Warehouse?
Types of Data Warehouse
Who needs Data warehouse?
Why We Need Data Warehouse?
Data Warehouse Tools
Chapter 2: Data Warehouse Architecture
Characteristics of Data warehouse
Data Warehouse Architectures
Datawarehouse Components
Query Tools
Chapter 3: ETL Process
What is ETL?
Why do you need ETL?
ETL Process
ETL tools
Chapter 4: ETL Vs ELT
What is ETL?
Difference between ETL vs. ELT
Chapter 5: Data Modeling
What is Data Modelling?
Types of Data Models
Characteristics of a physical data model
Chapter 6: OLAP
What is Online Analytical Processing?
Types of OLAP systems
Advantages and Disadvantages of OLAP
Chapter 7: Multidimensional Olap (MOLAP)
What is MOLAP?
MOLAP Architecture
MOLAP Tools
Chapter 8: OLAP Vs OLTP
What is the meaning of OLAP?
What is the meaning of OLTP?
Difference between OLTP and OLAP
Chapter 9: Dimensional Modeling
What is Dimensional Model?
Elements of Dimensional Data Model
Attributes
Difference between Dimension table vs. Fact table
Steps of Dimensional Modelling
Rules for Dimensional Modelling
Chapter 10: Star and SnowFlake Schema
What is Multidimensional schemas?
What is a Star Schema?
What is a Snowflake Schema?
Difference between Start Schema and Snowflake
Chapter 11: Data Mart
What is Data Mart?
Type of Data Mart
Steps in Implementing a Datamart
Chapter 12: Data Mart Vs Data Warehouse
What is Data Warehouse?
What is Data Mart?
Differences between a Data Warehouse and a Data Mart
Chapter 13: Data Lake
What is Data Lake?
Data Lake Architecture
Key Data Lake Concepts
Maturity stages of Data Lake
Chapter 14: Data Lake Vs Data Warehouse
What is Data Warehouse?
What is Data Lake?
Key Difference between the Data Lake and Data Warehouse
Chapter 15: What Is Business Intelligence?
What is Business Intelligence
Why is BI important?
How Business Intelligence systems are implemented?
Four types of BI users
Chapter 16: Data Mining
What is Data Mining?
Types of Data
Data Mining Process
Modelling
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