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Data Integration Is a Small(er) Vendor's Game: Report

• By David Ramel
• January 13, 2025

Despite their dominance in other areas of enterprise tech, the cloud's "Big 3" -- Amazon, Microsoft and Google -- have yet to conquer the data integration market.

A new Magic Quadrant report by research firm Gartner found the data integration landscape is thriving as organizations seek better ways to handle data for various needs, including operations, analytics and AI. It's also become a crowded market, to the detriment of big hyperscalers. Instead, in the "Leaders" quadrant, the cloud giants are eclipsed by lesser players, including Informatica, Oracle and IBM. (However, Microsoft does rank highly on the "ability to execute" axis and trails only those three companies above on the "completeness of vision" axis.)

Amazon Web Services (AWS) and Google, meanwhile, trail on the vision axis behind eight other leaders.

Gartner weighed in on why those cloud giants might not be dominating this space, which has seen the market share of the top five vendors shrinking from 71 percent in 2017 to 53 percent in 2023.

"One of the main reasons for this is that, as organizations migrate data integration to the cloud, they're evaluating modern cloud-native/SaaS vendors and public cloud hyperscalers (CSPs), including native cloud data ecosystems," the report said. "Another reason is that smaller vendors with more focused offerings, or those that target business users through innovative offerings, pricing, packaging and go-to-market strategies, continue to disrupt larger vendors. Vendors gaining market share have a common theme: They focus on leadership in specific data integration styles such as data virtualization, data replication or streaming; and/or they focus on data integration delivered as a native and managed cloud service. Vendors need to find the right balance between all-encompassing platform solutions and easily accessible point solutions to keep pace."

Gartner notes the products work on-premises, in a public or private cloud or in hybrid cloud implementations, often being consumed as Software-as-a-Service (SaaS).

Common use case scenarios or business problems addressed by tools in the space include:

• Data engineering -- Data integration by technical user personas to develop, manage and optimize data pipelines in analytical use cases
• Delivering modern data management architectures -- Data integration to enable modern data management design patterns like lakehouse, data fabric, data mesh and deliverables like data products
• Enabling less-technical data integration -- Data integration activities by less-technical user personas for various analytical demands of data, such as analytics and business intelligence (ABI), and data science use cases
• Operational data integration -- Data integration to implement various operational data integration use cases, such as consolidation of master data, delivering and using data hubs, interenterprise and partner data sharing, and application integration

While AI figures to be a transformative technology here, like everywhere else, it also figures into the common features of data integration tools, one of which Gartner lists as: "Augmentation features that leverage generative AI (GenAI) and prepackaged ML algorithms to auto-generate data pipeline code and documentation, optimize data integration operations (e.g., anomaly detection, autorecovery), and use natural language to query as well as transform data."

The firm also noted "the use of GenAI assistant is now standard in most data integration tools, allowing creation, observability and management of data pipelines through NLQ type interfaces "

While most Gartner research reports are behind a paywall, the company allows licensed-for-distribution editions from the vendors covered, easily found with a quick internet search.