The discipline of data integration comprises the practices, architectural techniques and tools for achieving the consistent access and delivery of data across the spectrum of data subject areas and data structure types in the enterprise to meet the data consumption requirements of all applications and business processes.

Data integration tools have traditionally been delivered via a set of related markets, with vendors in each market offering a specific style of data integration tool. In recent years, most of the activity has been within the ETL tool market. Markets for replication tools, data federation (EII) and other submarkets each included vendors offering tools optimized for a particular style of data integration, and periphery markets (such as data quality tools, adapters and data modeling tools) also overlapped with the data integration tool space. The result of all this historical fragmentation in the markets is the equally fragmented and complex way in which data integration is accomplished in large enterprises — different teams using different tools, with little consistency, lots of overlap and redundancy, and no common management or leverage of metadata. Technology buyers have been forced to acquire a portfolio of tools from multiple vendors to amass the capabilities necessary to address the full range of their data integration requirements.

This situation is now changing, with the separate and distinct data integration tool submarkets converging at the vendor and technology levels. This is being driven by buyer demands as organizations realize they need to think about data integration holistically and have a common set of data integration capabilities they can use across the enterprise. It is also being driven by the actions of vendors, such as those in individual data integration submarkets organically expanding their capabilities into neighboring areas, as well as by acquisition activity that brings vendors from multiple submarkets together. The result is a market for complete data integration tools that address a range of different data integration styles and are based on common design tooling, metadata and runtime architecture.