Red-hot data visualization vendor Tableau Software Inc. will attempt to position itself at the center of an ecosystem during its this seventh annual customer event in Seattle this week by showcasing an impressive lineup of partners that are building upon the company’s analytics platforms.

Alteryx Inc., the top sponsor of the summit, hopes to steal the show with a new version of its flagship data integration solution that attempts to make it easier for customer to access the capabilities of Tableau’s popular visualization platform. Graphics can now be appended with blended information directly through the Alteryx Analytics interface, which spares analysts the hassle of manually exporting their files every time they want to update a chart. The upgrade is complemented by new templates and samples that the firm says are designed to help users start delivering results faster.

Trifacta Inc., another fast-growing provider of data transformation software, also pledged its allegiance to Tableau with a one-way connector for exporting output. The San Francisco-based firm touts a similar pitch to Alteryx’s: It aims to make it easier for everyday workers to make sense of information from different sources and to drastically reduce the time it takes to glean insights.

The integration with Tableau extends that value proposition to the final presentation stage of the analytics lifecycle, essentially positioning Trifacta as the central link between raw data and decision-makers. MapR Technologies Inc. is situated firmly in the source data side of the analytics equation, but that didn’t stop it from setting up shop at Tableau Conference alongside Trifacta and the other startups in attendance. 

The company will demonstrate how its flagship Hadoop distribution can be used to scale an analytical environment across commodity servers. The presentation centers on a ten-node cluster running MapR-DB, a homegrown database built atop the Hadoop platform that the company says can ingest 100 data points per second using only four of the machines. The point of the exhibit is to prove that Hadoop, which was originally developed for batch processing, can also be used to handle real-time workloads.

Image via Tableau