My e-Notes about DataScience, Machine Learning, Python, Data Analytics, DataStage, DWH and ETL Concepts


Monday, 7 March 2016

Data Warehouse Approaches #2

Top-down approach(Inmon)

The top-down approach views the data warehouse from the top of the entire analytic environment.

The data warehouse holds atomic or transaction data that is extracted from one or more source systems and integrated within a normalized, enterprise data model. From there, the data is summarized, dimensionalized, and distributed to one or more “dependent” data marts. These data marts are “dependent” because they derive all their data from a centralized data warehouse.

Sometimes, organizations supplement the data warehouse with a staging area to collect and store source system data before it can be moved and integrated within the data warehouse. A separate staging area is particularly useful if there are numerous source systems, large volumes of data, or small batch windows with which to extract data from source systems.


The major benefit of a “top-down” approach is that it provides an integrated, flexible architecture to support downstream analytic data structures.
First, this means the data warehouse provides a departure point for all data marts, enforcing consistency and standardization so that organizations can achieve a single version of the truth. Second, the atomic data in the warehouse lets organizations re-purpose that data in any number of ways to meet new and unexpected business needs.

For example, a data warehouse can be used to create rich data sets for statisticians, deliver operational reports, or support operational data stores (ODS) and analytic applications. Moreover, users can query the data warehouse if they need cross-functional or enterprise views of the data.

On the downside, a top-down approach may take longer and cost more to deploy than other approaches, especially in the initial increments. This is because organizations must create a reasonably detailed enterprise data model as well as the physical infrastructure to house the staging area, data warehouse, and the data marts before deploying their applications or reports. (Of course, depending on the size of an implementation, organizations can deploy all three “tiers” within a single database.) This initial delay may cause some groups with their own IT budgets to build their own analytic applications. Also, it may not be intuitive or seamless for end users to drill through from a data mart to a data warehouse to find the details behind the summary data in their reports.

Bottom-up approach(Kimball)

In a bottom-up approach, the goal is to deliver business value by deploying dimensional data marts as quickly as possible. Unlike the top-down approach, these data marts contain all the data — both atomic and summary — that users may want or need, now or in the future. Data is modeled in a star schema design to optimize usability and query performance. Each data mart builds on the next, reusing dimensions and facts so users can query across data marts, if desired, to obtain a single version of the truth as well as both summary and atomic data.

The “bottom-up” approach consciously tries to minimize back-office operations, preferring to focus an organization’s effort on developing dimensional designs that meet end-user requirements. The “bottom-up” staging area is non-persistent, and may simply stream flat files from source systems to data marts using the file transfer protocol. In most cases, dimensional data marts are logically stored within a single database. This approach minimizes data redundancy and makes it easier to extend existing dimensional models to accommodate new subject areas.


The major benefit of a bottom-up approach is that it focuses on creating user-friendly, flexible data structures using dimensional, star schema models. It also delivers value rapidly because it doesn’t lay down a heavy infrastructure up front.
Without an integration infrastructure, the bottom-up approach relies on a “dimensional bus” to ensure that data marts are logically integrated and stovepipe applications are avoided. To integrate data marts logically, organizations use “conformed” dimensions and facts when building new data marts. Thus, each new data mart is integrated with others within a logical enterprise dimensional model.
Another advantage of the bottom-up approach is that since the data marts contain both summary and atomic data, users do not have to “drill through” from a data mart to another structure to obtain detailed or transaction data. The use of a staging area also eliminates redundant extracts and overhead required to move source data into the dimensional data marts.

One problem with a bottom-up approach is that it requires organizations to enforce the use of standard dimensions and facts to ensure integration and deliver a single version of the truth. When data marts are logically arrayed within a single physical database, this integration is easily done. But in a distributed, decentralized organization, it may be too much to ask departments and business units to adhere and reuse references and rules for calculating facts. There can be a tendency for organizations to create “independent” or non-integrated data marts.

In addition, dimensional marts are designed to optimize queries, not support batch or transaction processing. Thus, organizations that use a bottom-up approach need to create additional data structures outside of the bottom-up architecture to accommodate data mining, ODSs, and operational reporting requirements. However, this may be achieved simply by pulling a subset of data from a data mart at night when users are not active on the system.

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