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Inhaltsangabe
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
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Présentation de l'éditeur
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
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