by Angela Guess
A recent press release states, “Ryft, the technology leader in the acceleration of business analytics powered by cloud, hybrid and on-premises platforms, announced it is now providing Amazon Web Services’ customers smarter, faster data search and analysis capabilities with push-button ease of use on the Amazon F1 FPGA-accelerated instance. Now available on the AWS Marketplace, Ryft’s offerings give users the never-before-possible abilities to perform full PCRE2 Regular Expression searching on both pre- and post-index files and near-instant fuzzy search and matching on indexed and unindexed data, with a broader range of fuzzy search criteria. While organizations have made do with limited features and performance for business-critical analytics functions, these restrictions are increasingly detrimental to their ability to derive fast, actionable insights. Ryft’s technology on the Amazon F1 instance powers the next generation of tools that organizations need in order to analyze and understand all their data immediately, with no delay and no boundaries defined by analytics platform limitations.”
The release continues, “Today, Ryft released two Amazon Machine Images (AMIs) that deploy Ryft services on Amazon infrastructure: (1) Toolkit Powered by Ryft’s Heterogeneous Computing: Ryft’s developer toolkit enables users to instantly — and without a steep learning curve — integrate smarter, more sophisticated search and analysis capabilities into existing data analytics interfaces and/or applications. Toolkit powered by Ryft increases the speed and quality of analytics insights by: Seamlessly integrating with current analytics environment via a series of APIs and connectors — including programmatic interfaces, command line, RESTful JSON, ODBC/JDBC and more — to access the powerful FPGA-enabled data analysis functions needed for faster insights and smarter business decisions. Getting more accurate and actionable insights with significantly enhanced PCRE2 Regular Expression capabilities as well as fuzzy Hamming and Levenshtein (Edit Distance) search on both structured and unstructured data. Turning big data into small data by analyzing and thinning data in near real time with no data indexing, transformation or curation requirements.”
Read more at PR Newswire.
Photo credit: Ryft