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STRUCTURE OF DATA WAREHOUSE

& DATA MARTS

STRUCTURE OF DATA WAREHOUSE & DATA MARTS

SUBMITTED TO:MR. ASHOK WAHI

SUBMITTED BY:NIKISHA GUPTA

CHANDNI RASTOGISAKSHI JAIN

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DATA WAREHOUSEA subject-oriented, integrated, time-variant, non-updatable collection of data used in support of management decision-making processes.

Subject-oriented: Customers, patients, students, products.Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources.Time-variant: Can study trends and changes.Non-updatable: Read-only, periodically refreshed; never deleted.

A data warehouse is a home for your high-value data, or data assets, that originates in other corporate applications, such as the one your company uses to fill customer orders for its products, or some data source external to your company, such as a public database that contains sales information gathered from all your competitors.


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CLASSIFICATION OF DATA WAREHOUSE

Each of these classifications of data warehouses implements various aspects of an overall data warehousing architecture are:

✓ Data warehouse lite: A relatively straightforward implementation of a modest scope (often, for a small user group or team) in which you don’t go out on any technological limbs; almost a low-tech implementation.

✓ Data warehouse deluxe: A standard data warehouse implementation that uses advanced technologies to solve complex business information and analytical needs across a broader user population.

✓ Data warehouse supreme: A data warehouse that has large-scale data distribution and advanced technologies that can integrate various “run the business” systems, improving the overall quality of the data assets across business information analytical needs and transactional needs.


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This architecture assures that your data warehouse meets your user’s information requirements and focuses on the following business organization and technical-architecture presentation components:

✓ Subject area and data content: A subject area is a high-level grouping of data content that relates to a major area of business interests, such as customers, products, sales orders, and contracts.

✓ Data source: Data sources are very similar to raw materials that support the creation of finished goods in manufacturing.

✓ Business intelligence tools: The user’s requirements for information access dictate the type of business intelligence tool deployed for your data warehouse. Some users require only simple querying or reporting on the data content within a subject area; others might require sophisticated analytics. These data access requirements assist in classifying your data warehouse.

✓ Database: The database refers to the technology of choice leveraged to manage the data content within a set of target data structures.

✓ Data integration: Data integration is a broad classification for the extraction, movement, transformation, and loading of data from the data’s source into the target database.


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DATA WAREHOUSE LITE A data warehouse lite is a no-frills, bare-bones, low-tech approach to providing

data that can help with some of your business decision-making. No-frills

means that you put together, wherever possible, proven capabilities and

tools already within your organization to build your system.

Figure: A data warehouse lite has a narrow subject area focus.


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Denormalizing data from a single application restructures that data to make it more conducive to reporting needs.


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The low-tech approach to moving data into a data warehouse lite database backup tapes.


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The architecture of a data warehouse lite is built around straight-linemovement of data.

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DATA WAREHOUSE DELUXE

A data warehouse deluxe has a broader subject area focus than a data warehouse lite.



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A data warehouse deluxe often has a complicated architecture with many different collection points for data.


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DATA WAREHOUSE SUPREME

Intelligent agents are an important part of the push technology architecture of a data warehouse supreme.


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Sample architecture from a data warehouse supreme (although it can look like just about anything).

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A data warehouse might consist of more than one database, under the control of the overall warehousing environment.


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DATA MART

A data mart is simply a scaled-down data warehouse.The idea of a data mart is hardly revolutionary, despite what you might read on blogs and in the computer trade press, and what you might hear at conferences or seminars.

There are three main approaches to create a data mart:

✓ Sourced by a data warehouse (most or all of the data mart’s contents come from a data warehouse)

✓ Quickly developed and created from scratch

✓ Developed from scratch with an eye toward eventual integration


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Data marts sourced by a data warehouse Many data warehousing experts would argue (and I’m one of them, in this

case) that a true data mart is a “retail outlet,” and a data warehouse provides its contents.

The data sources, data warehouse, data mart, and user interact in this way:The data sources, acting as suppliers of raw materials, send data into the data

warehouse.The data warehouse serves as a consolidation and distribution center,

collecting the raw materials in much the same way that any data warehouse does.

Instead of the user (the consumer) going straight to the data warehouse, though, the data warehouse serves as a wholesaler with the premise of “we sell only to retailers, not directly to the public.” In this case, the retailers are the data marts.

The data marts order data from the warehouse and, after stocking the newly acquired information, make it available to consumers (users).


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The retail-outlet approach to data marts: All the data comes from a data warehouse.


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In a variation of the sourced-from-the-warehouse model, the data warehouse that serves as the source for the data mart doesn’t have all the information the data mart’s users need. You can solve this problem in one of two ways:

✓ Supplement the missing information directly into the data warehouse before sending the selected contents to the data mart.

✓ Don’t touch the data warehouse; instead, add the supplemental

information to the data mart in addition to what it receives from the data warehouse.


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Top-down, quick-strike data marts

There are three reasons to go the data-mart route:

✓ Speed: A quick-strike data mart is typically completed in 90 to 120 days, rather than the much longer time required for a full-scale data warehouse.

✓ Cost: Doing the job faster means that you spend less money; it’s that simple.

✓ Complexity and risk: When you work with less data and fewer sources over a shorter period, you’re likely to create a significantly less complex environment — and have fewer associated risks.


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A top-down, quick-strike data mart is a subset of what can be built if you pursue full scale data warehousing instead.


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Bottom-up, integration-orienteddata marts

Theoretically, you can design data marts so that they’re eventually integrated in a bottom-up manner by building a data warehousing environment (in contrast to a single, monolithic data warehouse).

Bottom-up integration of data marts isn’t for the fainthearted. You can do it, but it’s more difficult than creating a top-down, quick-strike data mart that will always remain stand-alone. You might be able to successfully use this approach . . . but you might not.


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SUBSETS OF INFORMATION FOR DATA MART

Geography-bounded data: A data mart might contain only the information relevant to a certain geographical area, such as a region or territory within your company.

Organization-bounded data: When deciding what you want to put in your data mart, you can base decisions on what information a specific organization needs when it’s the sole (or, at least, primary) user of the data mart. This approach works well when the overwhelming majority of inquiries and reports are organization-oriented. For example, the commercial checking group has no need whatsoever to analyze consumer checking accounts and vice versa.

Function-bounded data: Using an approach that crosses organizational boundaries, you can establish a data mart’s contents based on a specific function (or set of related functions) within the company. A multinational chemical company, for example, might create a data mart exclusively for the sales and marketing functions across all organizations and across all product lines.


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Market-bounded data: A company might occasionally be so focused on a specific market and the associated competitors that it makes sense to create a data mart oriented with that particular focus. This type of environment might include competitive sales, all available public information about the market and competitors (particularly if you can find this information on the Internet), and industry analysts’ reports, for example.


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Data mart or data warehouse?

If you start a project from the outset with either of the following premises, you already have two strikes against you:

“✓ We’re building a real data warehouse, not a puny little data mart.”

“✓ We’re building a data mart, not a data warehouse.”

Until you understand the following three issues, you have no foundation on which to classify your impending project as either a data mart or a data warehouse:

✓ The volumes and characteristics of data you need

✓ The business problems you’re trying to solve and the questions you’re trying to answer

✓ The business value you expect to gain when your system is successfully built


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IMPLEMENTING A DATA MART

There are the three keys to speedy implementation:

✓ Follow an iterative, phased methodology.

✓ Hold to a fixed time for each phase.

✓ Avoid scope creep at all costs.


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MARTS

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