* ETL or Batch Data Integration: ETL is typically used for getting data in and out of a repository (data mart, data warehouse) for analytical purposes, and often addresses data cleansing and quality as well as master data management (MDM) requirements. With the onset of Hadoop to cost-effectively address the collection and storage of structured and unstructured data, however, the relevance of traditional rows-and-columns-centric ETL approaches is now in question.
iPaaS attempts to solve many of the problems that legacy systems have not been able to do cost-effectively or within the faster requirements of agile-based development. iPaaS is a set of cloud-based services that enables both IT organizations and lines of business to develop, deploy, manage, govern and integrate applications and business systems.
Vendors provide the software and hardware infrastructure, as well as the tools for building/testing/deploying/monitoring and orchestrating integration flows. Solutions include pre-built connectors to support a variety of modern and legacy data sources and systems. While still early in enterprise IT adoption, iPaaS solutions are developed to meet the new cloud expectations of the business, built on modern lightweight and more flexible standards like JSON and REST, with the ability to scale in and out elastically when needed.
Since iPaaS abstracts the complexity, and in this case, the code, there is a perceived loss of functionality or flexibility for IT, but the trade-off is a gain in productivity.
What to Consider
In large organizations, moving to an iPaaS solution is often a step-by-step approach and many companies will retain ESB and other older architectures for a period of time as they modernize their application and data infrastructure. Here are some thoughts on how to approach iPaaS.
First, evaluate iPaaS providers for the following technical requirements:
- Metadata-driven integrations vs. programmatic approaches
- Drag-and-drop user experience that allows for some degree of self-service
- Pre-built connectivity (minimizing coding)
- Cloud-based management and monitoring, including comprehensive error management, transactional support, data transformation and other operations
- A hybrid deployment model that respects data gravity and allows processing to run close to the applications, regardless of where they reside.
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