A data warehouse is a Subject-Oriented, Integrated, Time-Variant, and Non-Volatile collection of data in support of management’s decision-making process.

It is also defined in many ways:-

A decision support database that is maintained individually from the organization’s operational database

A decision support database that is maintained individually from the organization’s operational database

A decision support database that is maintained individually from the organization’s operational database

A decision support database that is maintained individually from the organization’s operational database

But the key to operational data may or may not contain “time element”.

But the key to operational data may or may not contain “time element”.

But the key to operational data may or may not contain “time element”.

But the key to operational data may or may not contain “time element”.

It contains an element of time, explicitly or implicitly.

It contains an element of time, explicitly or implicitly.

It contains an element of time, explicitly or implicitly.

It contains an element of time, explicitly or implicitly.

Every key structure in the data warehouse,

Every key structure in the data warehouse,

Every key structure in the data warehouse,

Every key structure in the data warehouse,

Every key structure in the data warehouse,

Data warehouse data: It provides information from a historical perspective (e.g., past 5-10 years)

Data warehouse data: It provides information from a historical perspective (e.g., past 5-10 years)

Data warehouse data: It provides information from a historical perspective (e.g., past 5-10 years)

Data warehouse data: It provides information from a historical perspective (e.g., past 5-10 years)

Operational database: It operates on current value data

Operational database: It operates on current value data

Operational database: It operates on current value data

Operational database: It operates on current value data

The time horizon for the data warehouse is significantly longer than that of operational systems

The time horizon for the data warehouse is significantly longer than that of operational systems

The time horizon for the data warehouse is significantly longer than that of operational systems

The time horizon for the data warehouse is significantly longer than that of operational systems

The time horizon for the data warehouse is significantly longer than that of operational systems

When data is moved to the warehouse, it is converted.

When data is moved to the warehouse, it is converted.

When data is moved to the warehouse, it is converted.

When data is moved to the warehouse, it is converted.

Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

It is constructed by integrating multiple, heterogeneous data sources.

It is constructed by integrating multiple, heterogeneous data sources.

It is constructed by integrating multiple, heterogeneous data sources.

It is constructed by integrating multiple, heterogeneous data sources.

It is constructed by integrating multiple, heterogeneous data sources.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

It's organized around major subjects, such as customers, products, sales.

Supports information processing by providing a solid platform of consolidated, historical data for analysis.

Supports information processing by providing a solid platform of consolidated, historical data for analysis.

Supports information processing by providing a solid platform of consolidated, historical data for analysis.

Supports information processing by providing a solid platform of consolidated, historical data for analysis.

Non-Volatile

A physically separate store of data transformed from the operational environment.

Initial loading of data and access to data.

Initial loading of data and access to data.

Initial loading of data and access to data.

Initial loading of data and access to data.

Requires only two operations in data accessing:

Requires only two operations in data accessing:

Requires only two operations in data accessing:

Requires only two operations in data accessing:

It does not require transaction processing, recovery, and concurrency control mechanisms.

The operational update of data does not occur in the data warehouse environment.

The operational update of data does not occur in the data warehouse environment.

The operational update of data does not occur in the data warehouse environment.

The operational update of data does not occur in the data warehouse environment.

Heterogeneous DB vs Data Warehouse

Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis.

Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis.

Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis.

Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis.

Data warehouse : It is of update-driven and high performance

Data warehouse : It is of update-driven and high performance

Data warehouse : It is of update-driven and high performance

Data warehouse : It is of update-driven and high performance

When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set.

When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set.

When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set.

When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set.

Operational Database

An operational database is a database that is accessed and updated continually and usually handles the daily transactions for a business. Operational databases use an OLTP approach and are designed to be write-optimized.

Why Separate Data Warehouse?

Note: There are more and more systems that perform OLAP analysis directly on relational databases.

Note: There are more and more systems that perform OLAP analysis directly on relational databases.

Note: There are more and more systems that perform OLAP analysis directly on relational databases.

Note: There are more and more systems that perform OLAP analysis directly on relational databases.

Data Quality : Different sources typically use inconsistent data representations, codes, and formats that have to be reconciled.

Data Quality : Different sources typically use inconsistent data representations, codes, and formats that have to be reconciled.

Data Quality : Different sources typically use inconsistent data representations, codes, and formats that have to be reconciled.

Data Quality : Different sources typically use inconsistent data representations, codes, and formats that have to be reconciled.

Missing Data : Decision support requires historical data which operational DBs do not typically maintain

Missing Data : Decision support requires historical data which operational DBs do not typically maintain

Missing Data : Decision support requires historical data which operational DBs do not typically maintain

Missing Data : Decision support requires historical data which operational DBs do not typically maintain

Different functions and different data:

Different functions and different data:

Different functions and different data:

Different functions and different data:

Warehouse— It is tuned for OLAP: complex OLAP queries, multidimensional view , consolidation

DBMS— It is tuned for OLTP: access methods, indexing, concurrency control, recovery

DBMS— It is tuned for OLTP: access methods, indexing, concurrency control, recovery

DBMS— It is tuned for OLTP: access methods, indexing, concurrency control, recovery

DBMS— It is tuned for OLTP: access methods, indexing, concurrency control, recovery

Data Warehouse Architecture

Business Analysis Framework

Steps for its Design

First, having a data warehouse may provide a competitive advantage by presenting relevant information from which to measure performance and make critical adjustments to help win over competitors.



Second, a data warehouse can enhance business productivity because it can quickly and efficiently gather the information that accurately describes the organization.



Third, a data warehouse facilitates customer relationship management because it provides a consistent view of customers and items across all lines of businesses, all departments, and all markets.



Finally, a data warehouse may bring about the cost reduction by tracking trends, patterns, and exceptions over long periods consistently and reliably.



To design an effective data warehouse we need to understand and analyze business needs and construct a business analysis framework.



The construction of a large and complex information system can be viewed as the construction of a large and complex building, for which the owner, architect, and builder have different views.



These views are combined to form a complex framework that represents the top-down, business-driven, or owner’s perspective, as well as the bottom-up, builder-driven, or implementor’s view of the information system.



There are 4 views regarding the design of a data warehouse





Top-down view

It allows the selection of the relevant information necessary for the data warehouse. This information matches the current and future business needs.

Data source view

It exposes the information being captured, stored, and managed by operational systems.

Data warehouse view

It consists of fact tables and dimension tables.

Business query view

It sees the perspectives of data in the warehouse from the view of the end-user.





Building and using a data warehouse is a complex task because it requires business skills, technology skills, and program management skills.



Business skills

How to build extractors that transfer data from the operational system to the data warehouse?

How to build warehouse refresh software that keeps the data warehouse reasonably up-to-date with the operating system’s data?

Understanding and translating the business requirements into queries that can be satisfied by the data warehouse.



Technology skills

Data analysts are required to understand how to make assessments from quantitative information and derive facts based on conclusions from historical information in the data warehouse.



Program management skills

Involve the need to interface with many technologies, vendors, and end-users in order to deliver results in a timely and cost-effective manner.





Types Of Data Warehouse Models

Enterprise Warehouse

Collects all of the information about subjects spanning the entire organization.

Data Mart

A subset of corporate-wide data that is of value to specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart

Independent vs. dependent (directly from the warehouse) data mart.

Virtual Warehouse

It is a set of views over operational databases

Only some of the possible summary views may be materialized