Data Warehousing
Data Warehousing
• What is data warehouse?
• Functions
• Data Warehouse Vs Data Mart
• Database Vs Data Warehouse
• OLAP Operations
• Data Warehouse Architecture
• Applications
Data Warehouse:
• Repository of information collected from multiple sources, stored under a
unified schema, and which usually resides at a single site.
• Subject-oriented, integrated, time-variant, and non-volatile collection of data in
support of management’s decision making process.
Functions:
• Data cleaning
• Data transformation
• Data integration
• Data loading &
• Periodic data refreshing
Multidimensional database structure
Physical structure:
relational data store / multidimensional data cube
Multidimensional Data Model:
Views data in the form of data cube.
Fact numerical measures
Fact table
Cuboid, base cuboid, apex cuboid
Star, snowflake and fact constellation schema
OLAP Operations:
• Roll-up
• Drill-down
• Slice
• Dice
• Pivot
Data Warehouse Architecture:
Architectural Diagram
Steps for designing a warehouse :
•Choose a business process to model
(eg) orders, sales, shipments
•Choose the grain of the business process
(eg) individual transactions, individual snapshots etc.
•Choose the dimensions that will apply to each fact table record
(eg) time, item, customer, supplier
•Choose the measures that will populate each fact table record
(eg) numeric quantities like dollars-cold, units-sold
Types of OLAP Servers:
• ROLAP servers
• MOLAP servers
• HOLAP servers
• Specialized SQL servers
Metadata Repository:
Data about data
Data that define data warehouse objects..
Contents:
• Structure of data warehouse
• Operational meta data
• Algorithms used for summarization
• Mappings from operational environment to data warehouse
• Data related to system performance
• Business metadata
Applications:
• Information Processing
• Analytical Processing
• Data Mining
• What is data warehouse?
• Functions
• Data Warehouse Vs Data Mart
• Database Vs Data Warehouse
• OLAP Operations
• Data Warehouse Architecture
• Applications
Data Warehouse:
• Repository of information collected from multiple sources, stored under a
unified schema, and which usually resides at a single site.
• Subject-oriented, integrated, time-variant, and non-volatile collection of data in
support of management’s decision making process.
Functions:
• Data cleaning
• Data transformation
• Data integration
• Data loading &
• Periodic data refreshing
Multidimensional database structure
Physical structure:
relational data store / multidimensional data cube
Multidimensional Data Model:
Views data in the form of data cube.
Fact numerical measures
Fact table
Cuboid, base cuboid, apex cuboid
Star, snowflake and fact constellation schema
OLAP Operations:
• Roll-up
• Drill-down
• Slice
• Dice
• Pivot
Data Warehouse Architecture:
Architectural Diagram
Steps for designing a warehouse :
•Choose a business process to model
(eg) orders, sales, shipments
•Choose the grain of the business process
(eg) individual transactions, individual snapshots etc.
•Choose the dimensions that will apply to each fact table record
(eg) time, item, customer, supplier
•Choose the measures that will populate each fact table record
(eg) numeric quantities like dollars-cold, units-sold
Types of OLAP Servers:
• ROLAP servers
• MOLAP servers
• HOLAP servers
• Specialized SQL servers
Metadata Repository:
Data about data
Data that define data warehouse objects..
Contents:
• Structure of data warehouse
• Operational meta data
• Algorithms used for summarization
• Mappings from operational environment to data warehouse
• Data related to system performance
• Business metadata
Applications:
• Information Processing
• Analytical Processing
• Data Mining
The data warehouse concept you have furnished above was very useful to me. Thanks for sharing.
ReplyDeleteRegards,
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