Unit II - DataWarehouse
• Data warehousing provides architectures and tools for business executives to systematically
understand, and use their data to make strategic decisions. Data warehouse systems are valua
today’s competitive, fast-evolving world.
• A data warehouse refers to a data repository that is maintained separately from an organization’
operational databases. Data warehouse systems allow for the integration of a variety of applicati
• The four keywords—subject-oriented, integrated, time-variant, and nonvolatile—distin
warehouses from other data repository systems, such as relational database systems, transaction
systems, and file systems.
Dr.Priya Govindarajan
,Key features:
• Subject-oriented: A data warehouse is organized around major subjects such as custome
product, and sales - It focuses on the modeling and analysis of data for decision makers.
• Integrated: A data warehouse is usually constructed by integrating multiple heterogeneous sourc
relational databases, flat files, and online transaction records.
• Time-variant: Data are stored to provide information from a historic perspective (e.g., the past 5
Every key structure in the data warehouse contains, either implicitly or explicitly, a time element.
• Nonvolatile: A data warehouse is always a physically separate store of data transformed from the
data found in the operational environment. It usually requires only two operations in data access
loading of data and access of data.
Dr.Priya Govindarajan
,• The construction of a data warehouse requires data cleaning, data integration, and data con
The utilization of a data warehouse often necessitates a collection of decision-support technologies.
“knowledge workers” (e.g., managers, analysts, and executives) to use the warehouse to q
conveniently obtain an overview of the data, and to make sound decisions based on informa
warehouse.
• Data warehousing is also very useful from the point of view of heterogeneous database integration
• Query-driven approach, requires complex information filtering and integration processes, and com
local sites for processing resources.
• Update-driven approach in which information from multiple, heterogeneous sources is integrated
in advance and stored in a warehouse for direct querying and analysis.
Dr.Priya Govindarajan
, Differences between Operational Database Systems and DataWarehouses
• The major task of online operational database systems is to perform online transaction and query
These systems are called online transaction processing (OLTP) systems. They cover most of the
operations of an organization such as purchasing, inventory, manufacturing, banking, payroll, regis
accounting.
• Data warehouse systems, on the other hand, serve users or knowledge workers in the role of da
and decision making. Such systems can organize and present data in various formats in order to ac
the diverse needs of different users. These systems are known as online analytical processin
systems.
The major distinguishing features of OLTP and OLAP :
Users and system orientation: An OLTP system is customer-oriented and is used for transaction
processing by clerks, clients, and information technology professionals. An OLAP system is market-o
is used for data analysis by knowledge workers, including managers, executives, and analysts.
Dr.Priya Govindarajan
• Data warehousing provides architectures and tools for business executives to systematically
understand, and use their data to make strategic decisions. Data warehouse systems are valua
today’s competitive, fast-evolving world.
• A data warehouse refers to a data repository that is maintained separately from an organization’
operational databases. Data warehouse systems allow for the integration of a variety of applicati
• The four keywords—subject-oriented, integrated, time-variant, and nonvolatile—distin
warehouses from other data repository systems, such as relational database systems, transaction
systems, and file systems.
Dr.Priya Govindarajan
,Key features:
• Subject-oriented: A data warehouse is organized around major subjects such as custome
product, and sales - It focuses on the modeling and analysis of data for decision makers.
• Integrated: A data warehouse is usually constructed by integrating multiple heterogeneous sourc
relational databases, flat files, and online transaction records.
• Time-variant: Data are stored to provide information from a historic perspective (e.g., the past 5
Every key structure in the data warehouse contains, either implicitly or explicitly, a time element.
• Nonvolatile: A data warehouse is always a physically separate store of data transformed from the
data found in the operational environment. It usually requires only two operations in data access
loading of data and access of data.
Dr.Priya Govindarajan
,• The construction of a data warehouse requires data cleaning, data integration, and data con
The utilization of a data warehouse often necessitates a collection of decision-support technologies.
“knowledge workers” (e.g., managers, analysts, and executives) to use the warehouse to q
conveniently obtain an overview of the data, and to make sound decisions based on informa
warehouse.
• Data warehousing is also very useful from the point of view of heterogeneous database integration
• Query-driven approach, requires complex information filtering and integration processes, and com
local sites for processing resources.
• Update-driven approach in which information from multiple, heterogeneous sources is integrated
in advance and stored in a warehouse for direct querying and analysis.
Dr.Priya Govindarajan
, Differences between Operational Database Systems and DataWarehouses
• The major task of online operational database systems is to perform online transaction and query
These systems are called online transaction processing (OLTP) systems. They cover most of the
operations of an organization such as purchasing, inventory, manufacturing, banking, payroll, regis
accounting.
• Data warehouse systems, on the other hand, serve users or knowledge workers in the role of da
and decision making. Such systems can organize and present data in various formats in order to ac
the diverse needs of different users. These systems are known as online analytical processin
systems.
The major distinguishing features of OLTP and OLAP :
Users and system orientation: An OLTP system is customer-oriented and is used for transaction
processing by clerks, clients, and information technology professionals. An OLAP system is market-o
is used for data analysis by knowledge workers, including managers, executives, and analysts.
Dr.Priya Govindarajan
