Kdb+ and the Internet of Things/Big Data

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big-data-picKdb+ is a leader in what is unarguably the most demanding big
data market: financial trading and risk management. The company is
now targeting other environments. It has already achieved initial success in smart metering and pharmaceuticals/healthcare and the technology is well-suited to a variety of other environments including preventative maintenance (asset management), smarter cities, the connected car and other such environments where the collection and analysis of time-series based data is important.

Kx has been fortunate as result of the recent rise in the Internet of Things (IoT). The company’s focus is on offering the best possible performance for its chosen (financial) market. It just happens that the (big data) issues faced by that market are exactly analogous to many IoT environments. Not only is kdb+ suitable for implementation for many of these use cases but, more importantly, the product has significant technical advantages in these areas. As a result, there may be significant expansion of the company’s user base within the IoT domain.

Kdb+ is a column-based relational database with extensive in-memory capabilities, developed and marketed by Kx Systems. A new research paper is now available that details the use of kdb+ for IoT applications – “Kdb+ and the Internet of Things/Big Data.”

Download this whitepaper today to learn how Kx and their flagship product kdb+ are well positioned to capitalize on the burgeoning IoT industry. The following topics are considered in the paper:

  • Executive summary
  • The product
  • Architecture
  • Use cases
  • Performance and competition
  • About Kx Systems

The company is poised to exploit the capabilities of kdb+ in other big data markets and, especially, with respect to the IoT. Kx Systems has already started to make inroads in this area, as discussed in the paper (by means of case studies). Kx Systems has, perhaps serendipitously, built a platform that makes it ideal for many big data analytic requirements.

Download this white paper from the insideBIGDATA White Paper Library.

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