Data Integration Buyer's Guide

On-Premise vs. Cloud Integration Tools; What’s the Difference?

On-Premise vs. Cloud Integration Tools; What's the Difference?

On-Premise vs. Cloud Integration Tools; What's the Difference?

Data Integration software tools are vital to taking advantage of growing data volumes. Organizations increasingly view these solutions as necessary for data procurement, governance and overall Data Management. It’s only natural that enterprises lean on integration tools more and more given the velocity with which data currently moves. However, this has put data-driven businesses in a tough situation, gauging whether they should continue on with legacy offerings that, although have served them well in the past, are struggling to keep up with demand, and forward-thinking self-service integration solutions that utilize the many advantages of the cloud.

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Using cloud environments for Data Integration has numerous benefits. Data access becomes a strength as opposed to a glaring weakness when users are forced to wait on permissions from IT. In addition, agility to move from project to project increases, and the facilitation of integration between virtually any data source ensues. This is important in a time where data type changeover is the name of the game and new resources are coming online at warp speed. The cost benefit is large as well, since users can save time and resources by working at their own pace.

According to Gartner, iPaaS will be the integration platform of choice for new integration projects in the very near future, and from an annual revenue perspective, will leave traditional application integration suites in the dust. Gartner sees this trend developing as a result of challenges that application managers are facing when integrating hybrid application portfolios, making it more difficult for them to provide easy access to the data that lies within business systems.

Proliferation of cloud and mobile technologies is a driving factor behind this expanding sector of self-service tools. The need to keep up with these fast-paced technologies puts the future of Data Integration squarely in self-service functionality. The self-serve method also allows organizations to pivot to any new and emerging technologies that may not yet be in sight. This method helps to enhance the speed of business, with IT shifting focus to hold a more support-laden role in the enterprise.

Though iPaas is considered the ‘Data Integration of the future’, it is possible that adoption will be hampered by a lack of standards and skills, incomplete offerings and the trouble that organizations may have federating it with legacy on-premise tools. In addition, there are sure to be security and privacy concerns with iPaaS just like there are with every other cloud-based solution offering. In the same breath too is the adoption conundrum. If organizations are still able to rig their data architectures to get by using legacy technologies, what’s the motivation to overhaul existing systems, especially considering the expenses that come along with this.

Accessing data doesn’t just mean having a unified view of it all. It needs to be in one place where an analytics program can reach it. That involves moving data from one place to another, usually from storage systems into a data warehouse capable of analyzing it. The most widely practiced method for doing this remains ETL (extract, transform, load).

Recent fragmentation in the legacy Data Integration market made us wonder whether or not traditional integration tools were becoming obsolete before our very eyes. With this in mind, we recently asked the crowd whether or not they believed Data Integration as we’ve known it was dying. To our surprise, the answer was a resounding no, and it appears that legacy tools are still being used in many verticals as enterprises prepare for the next wave in data tools. But just because ETL Data Integration solutions are still being used in data architectures all around doesn’t mean that they’re not frustrating to use.

Innovative vendors are looking to get out ahead of what is becoming a major problem for enterprises, and that is data latency. Organizations, now more than ever, need integration tools that enable their end-users to do self-service both on-premise and in the cloud. Modern integration solutions, such as those that are branded as Data Virtualization, Integration Platform as a Service, or more commonly, self-service, provide agility that legacy offerings simply cannot match.

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