Viral Data in SOA: An Enterprise Pandemic (IBM Press)

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Download DB2. Powered by. Groups Members Discussions Leaderboards. Spurred by the need to eliminate duplication, cut costs and offer greener solutions, organizations have renewed their efforts to centralize IT systems and deploy shared services. A shared data resource exposed through a layer of services behaves more like a virus, unilaterally affecting all those that touch it.

This book deals with treating and preventing harmful data in a Service Oriented Architecture. How badly are you infected? SOA is big. Information on Demand is big. It also requires an unnecessary amount of system memory to load all data quality processes and variables that will slow the time for overall job processing.

For example, associative relationships for connecting agreements together should be processed here. Any new components must meet the criteria for reusability. Physical Data Integration Models 59 For example, gender or postal codes are considered business rules that can be applied as data quality rules against all data being processed.


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Check Postal Code Ranges 2. An example of a common component-transformation data integration subject area model is depicted in Figure 3. In addition to the simplicity of grouping data by subject area for understandability and maintenance, grouping data by subject area logically limits the amount of data carried per process because it is important to carry as little data as possible through these processes to minimize performance issues.

An example of a physical data integration subject area model is shown in Figure 3. Diagramming tools such as Visio require manual creation and maintenance to ensure that they are kept in sync with source code and Excel spreadsheets. By using a data integration package, existing data integration designs e. Moving from a logical design to a physical design using the same metadata in the same package speeds up the transfer process and cuts down on transfer issues and errors.

These physical data integration jobs are stored in the same metadata engine and can be linked to each other.

They can also be linked to other existing metadata objects such as logical data models and business functions. The capture of source-to-target mapping metadata with transformation requirements earlier in the process also increases the probability of catching mapping errors in unit and systems testing. In addition, because metadata capture is automated, it is more likely to be captured and managed.

Industry-Based Data Integration Models To reduce risk and expedite design efforts in data warehousing projects, prebuilt data models for data warehousing have been developed by IBM, Oracle, Microsoft, and Teradata.

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As the concept of data integration modeling has matured, prebuilt data integration models are being developed in support of those industry data warehouse data models. Prebuilt data integration models use the industry data warehouse models as the targets and known commercial source systems for extracts. Having industry-based source systems and targets, it is easy to develop data integration models with prebuilt source-to-target mappings.

An example of an industrybased data integration model is illustrated in Figure 3. Summary Data modeling is a graphical design technique for data. In data integration, data integration modeling is a technique for designing data integration processes using a graphical process modeling technique against the data integration reference architecture. This chapter detailed the types of data integration models—conceptual, logical, and physical—and the approach for subdividing the models based on the process layers of the data integration reference architecture.

This chapter also provided examples of each of the different logical and physical data integration model types. The next chapter is a case study for a bank that is building a set of data integration processes and uses data integration modeling to design the planned data integration jobs.

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Data integration modeling is based on what other modeling paradigm? List and describe the types of logical data integration models. List and describe the types of physical data integration models. Using the target-based design technique, document where the logical data quality logic is moved to and why in the physical data integration model layers. Using the target-based design technique, document where the logical transformation logic is moved to and why in the physical data integration model layers.

Case Study Overview Due to new regulatory reporting requirements, a small regional bank known as the Wheeler Bank needs to better understand its overall loan portfolio exposure. Currently, it has disparate customer, commercial loan, and retail source systems that would provide the data needed for the loan reporting requirements.

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New federal credit loan reporting regulations require that all banks loans are aggregated by customer on a monthly basis. To provide this ability to view all loans by customer, a data warehouse will be needed for reporting and analysis of a combined loan portfolio. This case study revolves around the design of the data integration processes necessary to populate a customer loan data warehouse and data mart for a bank to analyze loan performance. Figures 4. All these questions are typically addressed in the analysis and logical design.

Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture

This provides the next-level, big-picture view of the scope and boundary for the project and the system. Do you need both a conceptual data integration model and high-level data integration model?


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It is the same as to whether a project needs a conceptual and logical data model. These enterprise data integration models can be built from the project-based conceptual data integration models, again depending on the maturity and intentions of the organization. Now, the focus is on designing logical data integration models for each layer of the data integration reference architecture e. For our case study, there are only three sources: the customer hub, commercial loan, and retail loan.

It is best to put all three sources on the same diagram for the sake of simplicity. In a new data warehouse build-out, a typical data integration project can have from 20 to 30 sources, which at a conceptual and high level can potentially be displayed on one page, but not with any detail.

In addition, we will need to build three logical extract data integration models, one per source system. By reviewing the data models, a pattern can be determined for logical groupings for subject areas.

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Determine the Business Extraction Rules Determine what needs to occur to extract or capture the data from the source system. When extracting a limited set of data for a single application or database, it is highly probable that there will be the need to extend the application, or rewrite the application, or in the worst case, write another extract from the same source system. Data quality processes are those data integration processes that qualify and cleanse the data, based on technical and business process rules. The data quality design framework in Figure 4.

Identify Technical and Business Data Quality Criteria The data model contains attributes for which maintaining data quality is critical to ensure the level of data integrity. Note that those data quality checks in the shading in Figure 4. Check Customers 2.


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Check Addresses 3. Check Loans 4. Check Products 1. Check Loans 4 Check Products 1. As an organization matures in both Information Management and data governance processes, so will the business data quality checks in the data quality data integration model. Records that fail such tests should not be used for any purpose. In the high-level logical data integration model, transforms are broken into two subject areas—customer and loan—as portrayed in Figure 4. What other considerations should be reviewed for the target data model?

The logical transformation data integration model for the customer loan data warehouse is shown in Figure 4. Transform Customer 1. Transform Loan 1. They include conforming, calculation, splits, and lookup. Examples of each are shown in Figure 4. Although the focus of this book is data integration, there are data warehouse modeling architectural patterns that impact the design and architecture of data integration processes.

One is that most transforms from source to EDW enterprise data warehouse are simple conforms, whereas from the EDW to the data mart, they are mostly calculations and aggregations.