News Stay informed about the latest enterprise technology news and product updates.

Data integration software tools: Gartner Magic Quadrant names top vendors

Data integration software tool vendors need more than extract, transform and load functions to make Gartner's new Magic Quadrant.

Extract, transform and load (ETL) functionality alone is no longer enough to get a data integration software tools vendor ranked by Gartner.

The Stamford, Conn.-based analyst firm won't put out any more ETL Magic Quadrants, focusing instead on data integration platforms, according to Andreas Bitterer, research vice president. The firm released the first Magic Quadrant for data integration software tools last week, underlining a market trend toward rationalization of various integration methods, such as ETL, enterprise application integration (EAI), and enterprise information integration (EII), into one platform. 

Gartner evaluated companies that offer a platform with a broad spectrum of data integration capabilities, ideally with a common metadata repository and data quality functions, Bitterer explained. Platforms were ranked primarily on functionality for ETL, federation, replication, synchronization and process integration, as well as their capabilities, such as service-oriented architecture (SOA) readiness and support for unstructured and semi-structured data. The study identified two leaders -- but it's still a new, emerging market, Bitterer said.

"Vendor consolidation is preceding the convergence of single-purpose tools -- most vendors address the full breadth of delivery styles through multiple products, rather than through a single, comprehensive product," the study says. "At the same time, some of the newer vendors emerging in and around this market are starting out with metadata-driven architectures, promising to bypass single-purpose, connection-driven tools."

The vendors in the leaders quadrant -- IBM and Informatica -- received high marks for both vision and execution. Though the two were close in the rankings, IBM was out in front thanks to its acquisition of Ascential and the culmination of the much-anticipated Hawk project, recently officially announced as part of its new Information Server. Informatica has also been aggressively building out its platform, with this year's acquisition of data quality vendor Similarity Systems and its recently announced plans to acquire Itemfield to bolster capabilities around unstructured and semi-structured data. The study named SAS the sole vendor in the visionaries quadrant -- a strong contender, according to Bitterer, with a notable vision but less execution.

In the challengers quadrant, or those that ranked well in execution but less well in vision, were Business Objects and Pervasive. The other 12 vendors that made the quadrant were clustered in the niche quadrant, with lower marks on vision and execution, but enough capabilities to make the study. Notable names included Microsoft, Oracle and iWay, which are close to the dividing lines and poised to make their way into the leaders or challengers quadrants, Bitterer noted. The niche quadrant also included SAP, Sun Microsystems (thanks to the acquisition of SeeBeyond), Tibco, Sybase, Cognos, Group 1 Software, Embarcadero Technologies, ETI, and the lesser-known Ab Initio. Next year, the graphic could look very different, Bitterer said.

"This market is by no means stable. There will be more platform approaches by other vendors, moving beyond single-purpose integration tools," Bitterer said. "There may be some acquisitions in the works. There are some mega-vendors out there still missing pieces of a data integration platform, like some of the BI players, such as Cognos and Hyperion. It depends on how strategically important data integration becomes to them."

Bitterer cautioned companies purchasing a data integration platform not to base decisions on rankings alone. It's important to map requirements to available offerings and prioritize projects, he said, adding that the platform approach will ultimately offer major benefits.

"You want to have as few data integration tools as possible," Bitterer said. "It's easier to manage data integration and all of the related aspects if you have one single metadata repository and one set of rules for cleansing and profiling, rather than trying to maintain multiple ones. You also want to look at usability and look for tools with capabilities that address all your different sources and targets that you need to integrate."

Dig Deeper on Extract transform load tools

Start the conversation

Send me notifications when other members comment.

Please create a username to comment.