Data Warehousing

Data warehousing is the gathering of data across several networks that are converted into useful applications for computer users. The term fundamentally relates to the “warehousing of data,” or storing of data—but there is more to data warehouse processes than storing data. There are three major processes that occur in data warehousing: staging (or storing of data), integration (combining data across several places), and access (providing handy applications to network users).

 

Other discussions of data storage and processing concern data warehousing concepts such as data mining, online analytical processing, and business intelligence (BI). A data warehouse is a place to store transaction data. It is used for decision-making processes and can constitute more than one database. Decision support is a business tool that often contains the use of numerous graphs and helps business employees learn crucial information that could shape the growth of their businesses.

 

Data storage statistics, another warehousing element, show that at least 85% of Fortune 1000 companies considered data storage fifteen years ago. This number is likely to have increased. In most cases, the businesses that considered data storage have most likely converted their businesses to it by now.

 

Data mining refers to the entering of databases in order to find unique relationships. Data mining is used to increase sales and save expenses. Businesses generally attempt to find ways to meet certain demands of the public that will increase sales, or find ways to meet demands of the public by eliminating unwanted factors. Movie rental places often give free video rentals if you rent one movie. They also do “buy one, get one free” offers. Retail stores do this by slashing prices and guaranteeing free items if you purchase a certain number of a particular item. Holiday sales such as “After Thanksgiving,” “Black Week” (formerly “Black Friday”), Christmas sales and summer savings are ways that businesses make up for their financial slumps throughout the year—or make extra money at the year’s end.

 

There are five major elements of data mining:

 

• to gather, filter out, and transform data for storage in the data warehouse
• store and manage data
• grant access to stored data to technical professionals
• use software to analyze stored data
• present results in a graph or table

 

 

Online analytical processing, or online analysis processing, refers to the gathering and summarization of data. Metadata repositories refer to information about the data that are stored. Metadata repositories work similar to a library card catalog. The library card catalog tells you about the books within it, but the catalog is not a book. Metadata repositories can provide descriptions of the stored data but are not data themselves. Another element of warehousing involves the evolution of data storage from its basic file information to complex systems.

 

Data warehousing tutorials are programs used to help individuals learn as much as there is to know about data storage:

 

• managing data storage units
• benefits of data storage
• understanding data marts
• maintaining records
• other issues of data warehousing

 

Data tutorials also provide interview questions related to data storage on the following subjects:

 

• Abinitio
• MSAS
• Basic questions
• BO Designer
• Business intelligence (BI)
• Cognos
• Data integration
• DataStage
• ETL
• Impromptu
• Informatica
• MicroStrategy
• ReportNet

 

There are a number of data warehousing articles available.

 

“The Evolving Role of the Enterprise Data Warehouse in the Era of Big Date Analytics”

• “Extreme Status Tracking for Real Time Analysis”
• “Three ETL Compromises to Avoid”
• “The 10 Essential Rules of Dimension Modeling”
• “Judge Your BI Tool through Your Dimensions”
• “Drill Down to Ask Why” (Parts 1 and 2)
• “Better Business Skills for BI and Data Warehouse Professionals”
• “Dividing the World”
• “Three Ways to Capture Customer Satisfaction”
• “An Architecture for Data Quality”
• “Building a Foundation for Smart Applications”
• “The Matrix: Revisited”
• “Data Warehouse Check-Ups”
• “The Soul of the Data Warehouse” (Pts. 1-3)
• “To Be or Not to Be Centralized”
• “Divide and Conquer”
• “Behavior: The Next Marquee Application”
• “Backward in Time”
• “Digital Preservation”
• “The Special Dimensions of the ClickStream”
• “The Market Basket Data Mart”
• “Warehousing Without Borders”
• “Turbocharge Your Query Tools”
• “It’s Time for Time”
• “Data Warehouse Insurance”

 

 

There are also data warehousing books out on the subject matter. The following are a small sample:

 

• The Data Warehouse Toolkit: The Complete Guide to Dimension Modeling, Second Edition (2002) by Ralph Kimball and Margy Ross
• Data Warehousing Fundamentals for IT Professionals (2010) by Paulraj Ponniah
• Data Warehousing for Dummies (2009) by Alan R. Simon
• Data Warehouse Design: Modern Principles and Methodologies (2009) by Matteo Golfarelli and Stefano Rizzi
• Star Schema: The Complete Reference (2010) by Christopher Adamson
• A Manager’s Guide to Data Warehousing (2009) by Laura L. Reeves
• Data Warehousing Essentials (ABC’s of Data Warehousing and Data Mining) [2011] by Sudhir Warier

 

• Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture (2011) by Anthony Giordano
• Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2011) by Gordon S. Linoff and Michael J. Berry

 

Data warehousing is a subject that has yet to be exhausted. It seems that, just as businesses continue to play around with the idea, books continue to be written on new challenges the subject poses for large companies. One thing’s for sure: the subject is here to stay.