By Hugh Hyndman
If your company is not already drowning in data now, it will be soon. Gartner Inc. forecasts that 8.4 billion connected things will be in use worldwide in 2017, up 31 percent from 2016, and will reach 20.4 billion by 2020. In 2017, 6 million new things will be connected every day. Each one of them will transmit a stream of data, adding up to sensor data volumes that will dwarf today’s volumes. How your business uses this streaming data to compete in the market may determine its long-term success within the world of Industrial IoT.
These volumes impose significant challenges and pressures on solutions based on traditional relational database management systems (RDBMS) to ingest, process, and store this data. These systems were not built to handle such enormous volumes of data and this is resulting in significant and unsustainable increases in license fees, infrastructure, and operating costs for industrial businesses.
To tackle these challenges, organizations are turning to a high-performance database technology from Kx Systems which has been used by leading organizations to achieve low-latency and high-volume event processing. Kx’s combination of in-memory and on-disk database, its efficient columnar design, and highly-integrated query language adds up to the fastest analytics engine on the market.
In this paper, we discuss approaches and techniques for preserving your investment in your software and RDBMS technology while addressing the performance, scale, and cost challenges when working with high volumes of data. We describe three architecture patterns (upstream, midstream, and downstream) for implementing Kx alongside existing systems and processes, discuss their advantages and disadvantages, and provide case studies of these patterns in action.
Kx offers a suite of enterprise-level products centered around kdb+; the world’s fastest time series database. Kdb+ is optimized for ingesting, analyzing, and storing massive amounts of structured data. The combination of the columnar design of kdb+ and its in-memory capabilities means it offers greater speed and efficiency than typical relational databases. Its native support for time-series operations vastly improves both the speed and performance of queries, aggregation, and analysis of structured data.
One feature that really sets Kx apart is its ability to combine streaming, in-memory and historical data in one simple and unified platform; there is no requirement to acquire disparate components to build a hybrid solution.
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Hugh Hyndman is the Director of IoT Solutions at Kx Systems, based out of Toronto. Hugh has been involved with high-performance big data computing for most of his career. His current focus is to help companies supercharge their software systems and products by injecting Kx technologies into their stack. If you are interested in OEM or partnership opportunities, please contact Hugh through email@example.com.