Big Data Plumbing Startup Scores Backing
GridGain, a startup company that focuses on delivering the middleware resources for high performance big data applications, announced a new round of funding today to the tune of $2.5 million led by RTP Ventures. The company claims it will be using the new cash injection to continue development and growth in the real-time big data processing arena.
GridGain provides Java and Scala-based middleware that lets users build high performance cloud applications that work natively in any managed environment (clusters, grids, clouds, etc). Due to the way this middleware is tuned for both high performance and big data readiness, it is being pitched as a solution for real-time processing of large datasets.
Users can parallelize the processing by splitting computationally intensive tasks into smaller pieces for linear scalability and can also partition the data and keep it in-memory for data-intensive applications—both of which create a solid environment for both the real-time and the data-heavy nature of some analytics applications. They also emphasize that they can eliminate the need for manual deployment across distributed systems—an important element for those who are used to working with third party tools for this.
The company claims over 500 customers, including a number of Fortune 500s like Sony, Apple, Thompson Reuters and a number of others in finance, retail and beyond. They say that the differentiator is their ability to handle the tough scalability needs for applications that demand real-time processing in the face of expanding data sets.
According to Kirill Sheynkman who leads RTP Ventures, the real-time angle to their big data focus is what drew the investment. He said today that GridGain’s combination of compute and in-memory data grids into a coherent, scalable, real-time platform are the keys to the company’s viability. He also said in the release that the company’s ability to take “web-scale data analytics beyond ETL-like Hadoop scripts into the modern world” is another important technological differentiator.
For those curious about optimization of both the compute and storage angles for cloud-based computationally and data-intensive tasks, the company has provided a pretty decent overview (without the expected sales rhetoric) here.