Properly architected SOA presents both business functionality and data as abstracted services. Applied to data access, if access is abstracted as data services and the access code moved into supporting infrastructure, then problems can be addressed and changes can be supported throughout the environment in a much more loosely coupled and flexible manner. In essence, the data services layer provides a single abstracted point of access for all data access, update, and creation operations, providing a holistic view of the data models that the underlying persistence layer relies on. It acts as a bridge between the business services and the underlying data persistence layer; business users needn’t concern themselves with whether the data they are consuming originated in the database, an enterprise application, a file system, another company, or anywhere else for that matter. This promise of ubiquitously accessible data freed from the constraints of its sources is liberating for companies as they struggle with the challenges of systems integration.

A data services layer must provide an interface that exposes a standard set of reusable data services for reading and writing data, independent from the underlying data sources. The loose coupling it enables between applications using the services and the underlying data source providers lets database architects modify, combine, relocate, or even remove underlying data sources from the data services layer without requiring changes to the interfaces that the data services expose. As a result, the architects can retain control over the structure of their data while providing relevant information to the applications that need it. Over time, this increased flexibility eases the maintenance of enterprise applications.

Before the advent of SOA, developers built the capabilities that the abstracted data service layer could provide using manual coding, tightly embedding that code into the application under construction. Embedding such data access and data abstraction code directly into applications limits the flexibility and reusability of the resulting applications and, as a result, enterprises looked to traditional middleware such as Extract, Transform, Load (ETL), and Enterprise Application Integration (EAI) products to provide the capabilities of the data service layer from a middleware perspective. The ETL approach is best suited to static applications that don’t require the flexibility that SOA can provide. It can be costly, as well, and requires a high management overhead. The EAI approach centralizes data interaction logic, but still fails to provide the flexibility many organizations require from their data services layer.

Even when an organization is implementing true SOA, however, a poorly architected data services layer can lead to performance issues. In many cases, each application has its own database which contains a duplicate copy of business reference data such as customer information, product information, and inventory levels, as shown in Figure 1.

The organization must synchronize such databases on a regular schedule, which can lead to stale or inconsistent data between applications. In this case, even when an organization has exposed application functionality as loosely coupled services the persistence tier still limits the flexibility and reusability of the services. Incorporating a data services layer into the SOA implementation addresses this problem. Data services offer transaction and connection management to multiple applications, as shown in Figure 2.

The data services layer manages the relationships between the data services and ensures that each application is aware of all data changes, regardless of the cause of the change. Leveraging data services as infrastructure services beneath the business service abstraction increases reusability and flexibility, and shortens development and rollout time for new services. A well-architected data services layer relies on consistent, high-performance data access middleware that leverages industry-standard APIs such as ODBC, JDBC, ADO.NET, and SDO.

To sum it up, building a data services layer as part of an SOA implementation provides the infrastructure necessary to reap the full benefits of SOA. Specifically, it provides the following solutions to common data problems:

• Reduced reliance on hand-coded persistence logic: By building abstracted, shared data services, it’s possible to leverage best-of-breed data access middleware to support the data services as part of an architected plan.
• Inflexible integration is addressed: Instead of the point-to-point or hard-coded integration of traditional integration approaches, the data services layer provides for loose coupling at the persistence tier.
• Data query bottlenecks removed: The data services layer enables organizations to implement content-based routing techniques to avoid bottlenecks.
• Improved data consistency: The data services layer functions as a single locus for data access in the enterprise, helping an organization drive toward a single source of truth for its data. Implementing a data services layer helps ensure that applications always pull data from the correct sources and consistently provide appropriate information back to all applications.

Without doubt, architects who focus on SOA must take a hard look at solving the data access challenges in order to plan a successful, loosely coupled architecture. Accessing data in the various stores across the enterprise in the most efficient, flexible manner is the basis for building data services, and is thus critical for building all the services in the SOA implementation.