I don't normally do this, but I'm going to quote the entire first paragraph of the "Context" section of Gartner's Magic Quadrant for Data Integration Tools.
In 2016, we stated that data integration requirements had become so onerous that applications should no longer be permitted to be silos first and integrated second. The question now is, "Will this data be used in adjacent use cases?" and the answer is always "yes." During the past 18 months, the pace of information capture has continued to grow well beyond the capacity to analyze and use it. During the past three decades, information technology has swung back and forth between abundant capacity for processing, storage, memory and even networks (and being forgiving of poor design) versus data volumes and process demands that overwhelm any planned capacity. The pendulum has begun to swing back to a position where capacity is no longer abundant when compared to data volume and availability. Cloud providers are now encountering the same connectivity and management issues that on-premises providers encountered a decade ago. We have to get smarter.
Pretty much. The integration market has always been about technical adaptation -- matching data types, message types, transport types, platform types, and semantics -- but there's a cross-current of organizational adaptation that permeates the industry right now as well.
Companies with ETL tools and experienced developers who understand the semantics of business data? They're finding themselves in the "big data" era, hiring expensive twenty-somethings who understand the Hadoop ecosystem, but who don't know much about the business content, and who tend to code everything by hand.
Companies who are used to bulk/batch integration (the report says that 80%+ of organizations are still primarily bulk/batch-oriented) are trying to understand "data in motion," composite applications, and the automated inclusion of analytics and machine learning into integration processes.
There's no "one way" of doing integration. There hasn't been for years, in fact. That's one of the reasons we built our Omni-Gen platform and iWay tools to handle many different scenarios. You'll find specialization -- iWay Big Data Integrator focuses specifically on reducing the complexity of Hadoop implementations, for instance -- but all of our technologies have been deployed in ways that satisfy multiple integration requirements.
That comes through in this report. We think we've been very good about engaging with the market under these conditions, and we're happy with Gartner's statements about us, such as the positive experience (top 25% for this Magic Quadrant) and sense of partnership our customers have, the fact that we're "making big data normal," and the breadth of our product portfolio.
I know that most people want to check out the vendors first, but I encourage you to get the quadrant, check out the market definition, which covers many integration styles (it's at the beginning), and the Context and Market Overview sections at the end. It tells an interesting story.