Importance of in-memory analytics highlighted

As more firms look to pick up the pace of their big data analytics deployments this year, one of the key considerations for businesses will be how they can boost the speed of their solutions to give better real-time and predictive analytics results.

One option that is set to be increasingly popular in 2014 and beyond, therefore, is in-memory computing, which can offer firms a major speed advantage over more traditional disk-based analytics tools.

This has been highlighted recently by InformationWeek, which named in-memory specialist Kognitio as one of its top 16 big data analytics platforms of the year.

The publication noted that effective data analysis is a "do or die requirement" for today's businesses, so having the right tools to make this faster, easier and more cost-effective will be vital to the success of any organization, regardless of size or sector.

It therefore highly praised the Kognitio Analytical Platform for the way it helps with this, with particular attention being paid to the strong support offered to RAM-intensive deployments and the fact it offers customers the ability to run complex data analyses and processes in memory.

The platform is a true in-memory relational database management system with massively parallel processes, optimized specifically for analytics. Users of the solution are able to query massive amounts of data and receive useful insight in a fraction of the time it would take competing solutions to deliver results – something that is crucially important in an era where fast reactions count.

Commenting on the recognition by InformationWeek, president and chief executive of Kognitio Steve Millard said it is a "high honor" to be named as a top performer in such a competitive field.

He added: "As more companies discover the value of True In-Memory for advanced analytics and data science, they understand what Kognitio brings to the table. This greater awareness has led to increased sales, as organizations leverage our platform to take their big data projects off the drawing board and into production."