Enterprise Architecture (EA) and its one of the most important pillars - application architecture - is increasingly turning into a labyrinth governed by conflicting interpretations and peculiar changes. To deal with this rising complexity of EA models, organizations need a solution that facilitates a simple landscape and ensures high levels of security as well as governance at the same time.
To simplify enterprise architecture models, enterprises require a sound application integration strategy that helps them unify and analyze their data assets proactively for driving growth and innovation.
In last few decades, enterprise integration framework has gone through changes, and so has an application integration strategy. These changes, however subtle, have given rise to a new approach altogether. This blog gives you a brief idea about what the approach is and how it can be used to curb the complexities posed by enterprise integration models.
Prior to knowing the ins and outs of approach that can deal with rising convolutions of enterprise integration framework, one needs to know the rationale behind the “shift”.
The strategy employed to implement enterprise application integration has evolved, from point-to-point to data-driven.
Organizations with a few infrastructural components employed point-to-point integration models to establish a connection between their applications. Vendors such as Microsoft, Oracle, and SAP used point-to-point integration models back in age when business ecosystems were ruled by monolithic application suites.
However, with enterprises entering an era of unprecedented change for business, expansion and growth have become inevitable. And companies with a point-to-point integration approach, are incapable of growing beyond a limit. In truth, an infrastructure based on point-to-point integration quickly becomes unmanageable, brittle, and damaging to both the IT budget and organizational ability to meet current and changing business demands.
The need of the hour is to adopt an integration strategy that can handle a large number of applications and island of data, all at once, at one place. The role of data-driven application integration approaches such as data virtualization comes into play here. Its intuitive modular structure offers support to a plethora of standards, formats, and protocols than any other open source ESB and allows organizations to cope with rising complexity and other significant bottlenecks.
Data virtualization is a modern data integration approach that promises companies a smooth data-driven journey. Unlike in the traditional integration method, where data has to be moved physically to a new, consolidated location, data virtualization offers a real-time view of the consolidated data, leaving source data exactly where it is. Data virtualization solutions establish enterprise data access layer that provides centralized access to the enterprise’s important data sources. Business users can access data whenever they want simply by querying the data virtualization layer, which in turn gets the data from the applicable data sources.
Data virtualization layer acts as an abstraction layer as it separates the logical view of data from the physical representation. Meaning, it abstracts business users from complexities such as where the data is stored or what format it is in. This layer can be leveraged easily between original and derived data sources, Enterprise Service Bus (ESB), ETL processes, middleware, devices, and other applications whether on-premises or cloud-based, to provide flexibility between layers of information and business technology.
In addition, data virtualization enables self-service integration capability to provide right context to big data fabric, helping enterprises maximize performance and drive revenues.
The proliferation of applications and APIs has strained traditional point-to-point integration approaches. Data virtualization offers a modernized integration approach for companies to manage their data across hundreds and even thousands of applications in their app ecosystem. Not only it allows enterprises to ensure efficient API management, but also brings order to fragmentation and propagation of data across an enterprise ecosystem.