Home > Articles > Data > SQL Server

Steps Involved in Building a Data Warehouse

Most companies have realized that collecting transactional data is useful. In fact, it is tough to find any company that does not record their transactions. In short, if you need to make use of the data residing in some or all of your systems, you need to build a data warehouse, as discussed in this article by Baya Pavliashvili.
Like this article? We recommend

Like this article? We recommend

In another article in this series, I give you a crash course on populating a data warehouse after it is built. The term data warehousing is rather popular these days, despite the fact that many people don't know what it stands for. Therefore, it might be prudent to step back and give you a general idea of what a data warehouse (DW) is and what it takes to build one.

Most companies have realized that collecting transactional data is useful. In fact, it is tough to find any company (besides some of the old-fashioned "Mom-and-Pop" stores) that do not record their transactions. The data that has been collected for a number of years reside in various data sources—some in the mainframes, some in proprietary systems, and some in client-server applications. Also, each of these systems was probably built and is being maintained by different people.

The typical dilemma of today's IT managers is not how to collect the data, but how to use the data accumulated over the years. The answer might sound simple: Put everything in one place and run reports against that database. Well, the programmer who built the mainframe system left the company 10 years ago. The consultants that were hired to build the proprietary system have since moved on to other jobs as well. Finally, you're already running the reports against the client server system you use for daily data collection, but those reports are fairly rigid—after they're printed, you can't really change or customize them. Each time you need a specific report, you have to pay a premium rate for a week or two to the outside consultant or to your own programmer. What can you do?

The goal of a data warehouse is to provide your company with an easy and quick look at its historical data. Advanced OLAP (on-line analytical processing) tools let DW users generate reports at a click of a mouse and look at the company's performance from various angles. How much data you need to examine depends on the nature of your business.

Suppose you have a manufacturing plant that produces thousands of parts per hour. The type of information you might be interested in includes the number of defects per hour or per day. Although you might want to examine the number of defective parts this year against the same number five years ago, such a ratio probably wouldn't provide the best picture of the company's performance. On the other hand, if you're in a car rental business, you might want to examine the number of customers this month against the same number six months ago. If you need to analyze the purchasing trends for customers with various demographic backgrounds, you might wish to examine data collected for a number of years. In short, if you need to make use of the data residing in some or all of your systems, you need to build a data warehouse.

Building a Data Warehouse

In general, building any data warehouse consists of the following steps:

  1. Extracting the transactional data from the data sources into a staging area

  2. Transforming the transactional data

  3. Loading the transformed data into a dimensional database

  4. Building pre-calculated summary values to speed up report generation

  5. Building (or purchasing) a front-end reporting tool

Extracting Transactional Data

A large part of building a DW is pulling data from various data sources and placing it in a central storage area. In fact, this can be the most difficult step to accomplish due to the reasons mentioned earlier: Most people who worked on the systems in place have moved on to other jobs. Even if they haven't left the company, you still have a lot of work to do: You need to figure out which database system to use for your staging area and how to pull data from various sources into that area.

Fortunately for many small to mid-size companies, Microsoft has come up with an excellent tool for data extraction. Data Transformation Services (DTS), which is part of Microsoft SQL Server 7.0 and 2000, allows you to import and export data from any OLE DB or ODBC-compliant database as long as you have an appropriate provider. This tool is available at no extra cost when you purchase Microsoft SQL Server. The sad reality is that you won't always have an OLE DB or ODBC-compliant data source to work with, however. If not, you're bound to make a considerable investment of time and effort in writing a custom program that transfers data from the original source into the staging database.

Transforming Transactional Data

An equally important and challenging step after extracting is transforming and relating the data extracted from multiple sources. As I said earlier, your source systems were most likely built by many different IT professionals. Let's face it. Each person sees the world through their own eyes, so each solution is at least a bit different from the others. The data model of your mainframe system might be very different from the model of the client-server system.

Most companies have their data spread out in a number of various database management systems: MS Access, MS SQL Server, Oracle, Sybase, and so on. Many companies will also have much of their data in flat files, spreadsheets, mail systems and other types of data stores. When building a data warehouse, you need to relate data from all of these sources and build some type of a staging area that can handle data extracted from any of these source systems. After all the data is in the staging area, you have to massage it and give it a common shape. Prior to massaging data, you need to figure out a way to relate tables and columns of one system to the tables and columns coming from the other systems.

Creating a Dimensional Model

The third step in building a data warehouse is coming up with a dimensional model. Most modern transactional systems are built using the relational model. The relational database is highly normalized; when designing such a system, you try to get rid of repeating columns and make all columns dependent on the primary key of each table. The relational systems perform well in the On-Line Transaction Processing (OLTP) environment. On the other hand, they perform rather poorly in the reporting (and especially DW) environment, in which joining multiple huge tables just is not the best idea.

The relational format is not very efficient when it comes to building reports with summary and aggregate values. The dimensional approach, on the other hand, provides a way to improve query performance without affecting data integrity. However, the query performance improvement comes with a storage space penalty; a dimensional database will generally take up much more space than its relational counterpart. These days, storage space is fairly inexpensive, and most companies can afford large hard disks with a minimal effort.

The dimensional model consists of the fact and dimension tables. The fact tables consist of foreign keys to each dimension table, as well as measures. The measures are a factual representation of how well (or how poorly) your business is doing (for instance, the number of parts produced per hour or the number of cars rented per day). Dimensions, on the other hand, are what your business users expect in the reports—the details about the measures. For example, the time dimension tells the user that 2000 parts were produced between 7 a.m. and 7 p.m. on the specific day; the plant dimension specifies that these parts were produced by the Northern plant.

Just like any modeling exercise the dimensional modeling is not to be taken lightly. Figuring out the needed dimensions is a matter of discussing the business requirements with your users over and over again. When you first talk to the users they have very minimal requirements: "Just give me those reports that show me how each portion of the company performs." Figuring out what "each portion of the company" means is your job as a DW architect. The company may consist of regions, each of which report to a different vice president of operations. Each region, on the other hand, might consist of areas, which in turn might consist of individual stores. Each store could have several departments. When the DW is complete, splitting the revenue among the regions won't be enough. That's when your users will demand more features and additional drill-down capabilities. Instead of waiting for that to happen, an architect should take proactive measures to get all the necessary requirements ahead of time.

It's also important to realize that not every field you import from each data source may fit into the dimensional model. Indeed, if you have a sequential key on a mainframe system, it won't have much meaning to your business users. Other columns might have had significance eons ago when the system was built. Since then, the management might have changed its mind about the relevance of such columns. So don't worry if all of the columns you imported are not part of your dimensional model.

Loading the Data

After you've built a dimensional model, it's time to populate it with the data in the staging database. This step only sounds trivial. It might involve combining several columns together or splitting one field into several columns. You might have to perform several lookups before calculating certain values for your dimensional model.

Keep in mind that such data transformations can be performed at either of the two stages: while extracting the data from their origins or while loading data into the dimensional model. I wouldn't recommend one way over the other—make a decision depending on the project. If your users need to be sure that they can extract all the data first, wait until all data is extracted prior to transforming it. If the dimensions are known prior to extraction, go on and transform the data while extracting it.

Generating Precalculated Summary Values

The next step is generating the precalculated summary values which are commonly referred to as aggregations. This step has been tremendously simplified by SQL Server Analysis Services (or OLAP Services, as it is referred to in SQL Server 7.0). After you have populated your dimensional database, SQL Server Analysis Services does all the aggregate generation work for you. However, remember that depending on the number of dimensions you have in your DW, building aggregations can take a long time. As a rule of thumb, the more dimensions you have, the more time it'll take to build aggregations. However, the size of each dimension also plays a significant role.

Prior to generating aggregations, you need to make an important choice about which dimensional model to use: ROLAP (Relational OLAP), MOLAP (Multidimensional OLAP), or HOLAP (Hybrid OLAP). The ROLAP model builds additional tables for storing the aggregates, but this takes much more storage space than a dimensional database, so be careful! The MOLAP model stores the aggregations as well as the data in multidimensional format, which is far more efficient than ROLAP. The HOLAP approach keeps the data in the relational format, but builds aggregations in multidimensional format, so it's a combination of ROLAP and MOLAP.

Regardless of which dimensional model you choose, ensure that SQL Server has as much memory as possible. Building aggregations is a memory-intensive operation, and the more memory you provide, the less time it will take to build aggregate values.

Building (or Purchasing) a Front-End Reporting Tool

After you've built the dimensional database and the aggregations you can decide how sophisticated your reporting tools need to be. If you just need the drill-down capabilities, and your users have Microsoft Office 2000 on their desktops, the Pivot Table Service of Microsoft Excel 2000 will do the job. If the reporting needs are more than what Excel can offer, you'll have to investigate the alternative of building or purchasing a reporting tool. The cost of building a custom reporting (and OLAP) tool will usually outweigh the purchase price of a third-party tool. That is not to say that OLAP tools are cheap (not in the least!).

There are several major vendors on the market that have top-notch analytical tools. In addition to the third-party tools, Microsoft has just released its own tool, Data Analyzer, which can be a cost-effective alternative. Consider purchasing one of these suites before delving into the process of developing your own software because reinventing the wheel is not always beneficial or affordable. Building OLAP tools is not a trivial exercise by any means.

Summary

In this article I gave you an overview of what a data warehouse is and what it takes to build one. In my other article in this series on data warehousing, I discuss gathering data and populating the staging area in greater detail.

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020