Data Warehouse Notes

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A data warehousing is a technique for collecting and managing data from varied sources to provide meaningful business insights. It is a blend of technologies and components which allows the strategic use of data. Also known as Decision Support System (DSS)

Types of Data Warehouse

  • Enterprise Data Warehouse: Enterprise Data Warehouse is a centralized warehouse. It provides decision support service across the enterprise. It offers a unified approach for organizing and representing data. It also provides the ability to classify data according to the subject and give access according to those divisions.
  • Operational Data Store: Operational Data Store, which is also called ODS, are nothing but data store required when neither Data warehouse nor OLTP systems support organizations reporting needs. In ODS, Data warehouse is refreshed in real time. Hence, it is widely preferred for routine activities like storing records of the Employees.
  • Data Mart: A data mart is a subset of the data warehouse. It specially designed for a particular line of business, such as sales, finance, sales or finance. In an independent data mart, data can collect directly from sources.

Advantages of Data Warehouse:

  • Data warehouse allows business users to quickly access critical data from some sources all in one place.
  • Data warehouse provides consistent information on various cross-functional activities. It is also supporting ad-hoc reporting and query.
  • Data Warehouse helps to integrate many sources of data to reduce stress on the production system.
  • Data warehouse helps to reduce total turnaround time for analysis and reporting.
  • Restructuring and Integration make it easier for the user to use for reporting and analysis.
  • Data warehouse allows users to access critical data from the number of sources in a single place. Therefore, it saves user's time of retrieving data from multiple sources.
  • Data warehouse stores a large amount of historical data. This helps users to analyze different time periods and trends to make future predictions.

Disadvantages of Data Warehouse:

  • Not an ideal option for unstructured data.
  • Creation and Implementation of Data Warehouse is surely time confusing affair.
  • Data Warehouse can be outdated relatively quickly
  • Difficult to make changes in data types and ranges, data source schema, indexes, and queries.
  • The data warehouse may seem easy, but actually, it is too complex for the average users.
  • Despite best efforts at project management, data warehousing project scope will always increase.
  • Sometime warehouse users will develop different business rules.
  • Organizations need to spend lots of their resources for training and Implementation purpose.


Data warehouse is an information system that contains historical and commutative data from single or multiple sources.

  • A data warehouse is subject oriented as it offers information regarding subject instead of organization's ongoing operations.
  • In Data Warehouse, integration means the establishment of a common unit of measure for all similar data from the different databases
  • Data warehouse is also non-volatile means the previous data is not erased when new data is entered in it.
  • A Datawarehouse is Time-variant as the data in a DW has high shelf life.
  • There are 5 main components of a Datawarehouse. 1) Database 2) ETL Tools 3) Meta Data 4) Query Tools 5) DataMarts
  • These are four main categories of query tools 1) Query and reporting, tools 2) Application Development tools, 3) Data mining tools 4) OLAP tools
  • The data sourcing, transformation, and migration tools are used for performing all the conversions and summarizations.
  • In the Data Warehouse Architecture, meta-data plays an important role as it specifies the source, usage, values, and features of data warehouse data.
Union of all data martsA single business process
Data received from staging areaStar-join (facts & dimensions)
Queries on presentation resourceTechnology optimal for data access and analysis
Structure for corporate view of dataStructure to suit the departmental view of data
Organized on E-R model

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