Data integration is a process which involves taking data, from single or multiple sources in order to transform it into meaningful information which can be utilised by business executives, data analysts and various other enterprise users. It has changed the way businesses used to perform. With its smart processes and advanced features, it takes its users beyond the traditional ETL software services which were severely limited in their functionality.
Development of Data Integration
The first generation of ETL tools which were available to businesses were made of simple but quite expensive code generators which had limited functionality. Many of the companies who worked with and evaluated these tools eventually found it more effective if they developed their own custom integration codes, which led to the need for something better. Next came the second generation ETL software which did offer more functionality, but since it was primarily batch-oriented, it did not satisfy the need either. Over the years, ETL tools have indeed evolved in quite a few key areas, such as development, integration functionality and operational processing. But if was to be a more viable development platform to fulfill its multiple needs, it needed more developments, especially in the support for code management, debugging, version control, documentation generation etc. For operational processing, these tools have now been created with built in functionality which include error handling, run time statistics, recovery on restart and scheduling. With the advent of data integration, there has been a significant improvement in performance since the process works with the leverage of memory, parallelism and a number of data transport architectures.
Evolution of Data Integration Platforms over ETL Software
Over the years, data integration needs have indeed expanded beyond the core ETL uses. These days it is also used to perform a number of tasks like B2B integration, application and business process integration, cloud integration, master data management, data migration, its consolidation and inspection of data quality and cleansing etc. With that a number of integration categories emerged as well which have been built targeting specific uses and technologies.
Enterprise application integration (EAI)
EAI, which is also referred to as application integration is a subcategory, which supports interoperability among different applications. This is enabled via the web or data services which have been created using service-oriented architecture and industry standards like electronic data interchange. ESB is one of the most common architectural approaches for implementing EAI functionality.
Enterprise messaging system (EMS)
Enterprise information integration (EII)
Initially called data federation, EII used to provide a decent virtual view of data sources, although it had limited integration capabilities. But the current generation, known as data virtualization software, provides both data abstraction as well as data services layers to a wide variety of sources, which include structured, semi-structured and unstructured data.
This is also commonly referred to as integration platform as a service (iPaaS). It is a cloud based integration service which has evolved to provide real time interoperability between database and cloud based applications. These tools are deployed as cloud service for leveraging EAI and EMS functionality.
Data integration is important to every field of business these days. If you pick a good data integration software, its application integration capabilities enables IT departments to be more productive and to be more responsive to the needs of the business.