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DataWarehouseArchitecture.pdf
Data Warehouse Architecture Classification
G. Blaži�, P. Poš�i� and D. Jakši� Department of informatics-University of Rijeka/ Rijeka, Croatia
[email protected], [email protected], [email protected]
Abstract - The purpose of this study is to give an overlook and comparison of best known data warehouse architectures. Single-layer, two-layer, and three-layer architectures are structure-oriented one that are depending on the number of layers used by the architecture. In independent data marts architecture, bus, hub-and-spoke, centralized and distributed architectures, the main layers are differently combined. Listed data warehouse architectures are compared based on organizational structures, with its similarities and differences. The second comparison gives a look into information quality (consistency, completeness, accuracy) and system quality (integration, flexibility, scalability). Bus, hub-and-spoke and centralized data warehouse architectures got the highest scores in information and system quality assessment.
Key words: data warehouse architecture classification, organization structure, information quality, system quality
I. INTRODUCTION None of the business organization is the same. They
all have their own unique processes and actions. In the databases of this organizations, the data has to be in every moment updated and available for processing. To be able to perform analytical processing for business decision- making, the organization needs to have a data warehouse where certain data are firstly being processed, and then permanently stored for future actions. The data warehouses provide support to managers in the management and decision-making. Before the implementation of a data warehouse, it is necessary to determine the criteria to be satisfied, and on the basis of determined, select the appropriate data warehouse architecture that satisfies the needs of organization.
The purpose of this paper is to give an overlook and comparison of best known data warehouse architectures. The main features for each architecture will be presented, and architectures will be compared based on organizational structures, as well as on system and information quality.
II. DATA WAREHOUSE ARCHITECTURE CLASSIFICATION
The authors [1] gave the best overlook and explanation of various data warehouse architectures. They put data warehouse architectures into two categories. The first one includes single-layer, two-layer and three-layer
architectures that are structure-oriented one and are depending on the number of layers used by the architecture. The second classification consists of independent data marts architecture, bus, hub-and-spoke, centralized and distributed architecture where the main layers are differently combined.
A. Single-Layer Architecture Single-layer architecture is not frequently used in
practice. The goal is to minimize the amount of stored data by removing data redundancies. Data warehouse is virtual and implemented as a multidimensional view of operational data. The main weakness of a single-layer architecture is its failure to separate the analytical and transactional data processing.
B. Two-Layer Architecture In the two-layer architecture, there is a separation
between two layers: a layer of data sources and data warehouse layer. Although it is called a two-layer architecture to emphasize the separation of the two layers, it is actually consisted of four data flow stages: source layer, data staging, data warehouse layer and analysis. In contrast to single-layer architecture, in this one there is a separation between analytical and transactional data
Figure 1 Single-Layer Architecture [1]
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processing.
C. Three-Layer Architecture Three-layer architecture is characterized by the fact
that three layers are physically implemented: source layer, reconciled layer and data warehouse layer. Reconciled data layer materializes operational data after integrating and cleansing source data. As a result of passing through the reconciled layer, data is integrated, consistent,
accurate, correct, and detailed.
D. Independent Data Marts Architecture As the name implies, independent data marts
architecture consists of various data marts being separately designed and implemented, in another words, they are not integrated. Data marts usually have inconsistent data definitions and use different dimensions and measures, making data analysis difficult. This architecture is usually replaced by another one in order to achieve better data integration and cross-reporting.
E. Bus Architecture The bus architecture is recommended by Ralph
Kimball and is similar to independent data marts architecture with significant difference – data marts are logically integrated and there is an enterprise-wide view of information.
F. Hub-And-Spoke Architecture The hub-and-spoke architecture consists of data
sources, reconciled data and data marts. Enterprise data warehouse, called the hub, is created with a set of data marts, called spokes. Atomic, normalized data is stored in a reconciled layer that feeds a set of data marts made of summarized data in multidimensional form. In hub-and- spoke architecture the emphasis is on scalability and extensibility, as well as on retrieving large amounts of information.
G. Centralized Architecture The centralized architecture is recommended by Bill
Inmon. This architecture can be seen as a specific implementation of hub-and-spoke architecture, where in contrast to hub-and-spoke, there is no dependent data marts. It consists of one centralized data warehouse which contains integrated data and data marts.
Figure 2 Two-Layer Architecture [1]
Figure 3 Three-Layer Architecture [1] Figure 4 Independent Data Marts Architecture [1]
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H. Distributed Architecture The distributed architecture is sometimes adopted in
dynamic contexts where existing data warehouses should be integrated to provide a single solution. Each data warehouse/data mart is in this architecture logically or physically integrated using joint keys, global metadata, distributed queries, and other methods.
III. DATA WAREHOUSE ARCHITECTURE COMPARISON
We start with comparison based on organizational structure. Visible features in the organizational structures of architectures were taken as comparison criteria. Is the source data being processed with ETL tools before being stored? Are the users getting data directly from data warehouse, or through data marts? Do the data marts depend on data warehouse?
In Table 1 the results are presented. In all architectures, except in single-layer architecture, data is stored and retrieved only after it has been processed by ETL tools. In single-layer and centralized architecture, data is accessed directly from data warehouse. In all other architectures, data for specific business area is being retrieved from data marts. If there is a data mart after data warehouse in the organizational structure, we can say that data mart depends on data warehouse. We can found independent data marts in independent data marts
Figure 5 Bus Architecture [2]
Figure 6 Hub-And-Spoke Architecture [1]
Figure 7 Centralized architecture [2]
Figure 8 Distributed Architecture [1]
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architecture, bus, centralized, and distributed architecture. Hub-and-spoke architecture has a data warehouse made of set of data marts.
For the second comparison we used numeric tables [3] to take a look into consistency, completeness, and accuracy of information, and integration, flexibility, and scalability of system. Based on these tables, Chart 1 and Chart 2 were created, representing results for independent data marts architecture, bus, hub-and-spoke, centralized and distributed architecture.
In information quality assessment on Chart 1, independent data mart architecture has the lowest results. The next one is distributed architecture. For bus, hub-and- spoke and centralized architecture, there are similar results. Independent data marts architecture has relatively small result for information completeness, what confirms the fact that this architecture is particularly weak in providing all data needed to make decisions. Hub-and- spoke and centralized architectures have higher values for information completeness. These architectures provide more comprehensive data source in decision support. The bus architecture has proved to be the best when it comes to the information accuracy.
In system quality assessment on Chart 2, the average results are analog to results in information quality assessment. Independent data marts architecture got also in this case the lowest results, and that is why this architecture is soon being replaced by another architecture. The distributed architecture has some better results. Bus, hub-and-spoke and centralized architectures also achieved similar results. Although centralized architecture got the highest result for scalability, bus and hub-and-spoke architectures have proven to be the best when it comes to all three measures of system quality.
In [4], [5] and [6] we can find more features that were used to assess the success of the architectures. The given conclusions are similar to ours.
IV. CONLUSION This paper gave an overlook of best-known data
warehouse architectures: single-layer, two-layer, and three-layer architecture, independent data marts architecture, bus, hub-and-spoke, centralized and
TABLE 1 ORGANIZATION STRUCTURE COMPARISON
Single-layer architecture
Two-layer architecture
Three-layer architecture
Independent data marts architecture
Bus architecture
Hub-and- spoke architecture
Centralized architecture
Distributed architecture
ETL tools are used in processing data.
NO YES YES YES YES YES YES YES
The users are getting data directly from data warehouse.
YES NO NO NO NO NO YES NE
Data marts are used in accessing data.
NO YES YES YES YES YES NO YES
Data marts depend are depending on data warehouse.
- YES YES NO NO YES/NO - NO
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Distributed architecture
Centralized architecture
Hub-and-spoke architecture
Bus architecture
Independent data marts architecture
Chart 1 Information quality assessment
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Distributed architecture Centralized architecture Hub-and-spoke architecture Bus architecture
Independent data marts architecture
Chart 2 System quality assessment
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distributed architecture. Looking at the organizational structures of these architectures, we can conclude that in the beginning there is source data. Data is then being processed by ETL tools (extraction, transformation, load) and then stored in data warehouse. Data marts can follow data warehouse for retrieving data required for analysis of particular business area. Using different tools, end users receive the required information.
The contribution of this paper is in comparison of best known data warehouse architectures. Bus, hub-and-spoke, and centralized architectures have shown the best results in assessing information quality, as well as in system quality assessment. The reason for that is that these architectures have developed over time and have become similar. We believe that architectures in the future will be very similar. Over time, there will be less disadvantages and every architecture will successfully satisfy needs of their clients.
For further work on this paper, one can pay attention to the future of data warehouse, due to the increasing popularity of NoSQL databases.
REFERENCES [1] M. Golfarelli, S. Rizzi, Data Warehouse Design: Modern
Principles and Methodologies, McGraw Hill, 2009, pp. 7-14. [2] Il-Yeol Song, „Data warehousing systems: foundations and
architecture“, Drexel University, available: http://www.cis.drexel.edu/faculty/song/courses/info%20607/DWS -Foundations-Architecture.pdf, 6.5.2016.
[3] H. J. Watson, T. Ariyachandra, “Data Warehouse Architectures: Factors in the Selection, Decision and the Success of the Architectures“, July 2005, pp. 39-40.
[4] T. Ariyachandra, H. J. Watson, “Which Data Warehouse Architecture Is Most Successful?”, Business Intelligence Journal Vol. 11, No. 1 2006, pp. 4-6.
[5] T. Ariyachandra, H. J. Watson, “Which Data Warehouse Architecture is Best?”, Communications of the ACM October 2008, Vol. 51 No. 10, pp. 146-147.
[6] Moh’d Alsqour, K. Matouk, M. L. Owoc, “A survey of data warehouse architectures – preliminary results”, Computer Scuence and Information Systems (FedCSIS), 2012 Federated Conference, pp. 1121-1126. IEEE, 2012.
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