Businesses want it all. They want fast, accurate and actionable information from their data. IT wants it all as well—it wants a system that has extremely high performance metrics while also being flexible and easy to use. Used to be that neither business nor IT could have what it wanted, but that’s not the case anymore.

We published our new data analytics Open API Library today to deliver an open interface that is simple, easy-to-use and flexible and supercharges the purpose-built and high performance Ryft ONE hardware to provide the performance metrics organizations need in today’s real-time business world.

big data open api ryft

Businesses and IT buyers are weary with systems as they stand now. Closed systems provide the performance metrics they need, but they are often costly and difficult to use. Open systems have the flexibility and simplicity that they need, but the performance is often significantly degraded.

This has led to data scientists and business analysts to compromise—and in ways that are increasingly becoming detrimental to the run of business. Batch and streaming data couldn’t be analyzed simultaneously; indexing and ETL slowed down an already slow data pipeline; strict security controls for data protection added latency to the process. All this led to users waiting weeks or months to get answers from their data, but by then, the data is out of date.

With the new Ryft Open API Library and the Ryft ONE, users don’t have to choose between performance and ease of use. The open API seamlessly integrates the Ryft ONE into any existing data analytics system to harness the benefits of the current environment while bolstering analytics efforts with the appliance’s 100X performance improvements. With a growing set of primitives—such as exact search, fuzzy search and term frequency—the open API also supports the high-level programming language of the user’s choice, including C/C++, Java, Python, R, Scala and more.

The Ryft Open API Library, which can be downloaded on our website, enables organizations to accelerate their analytics pipeline and speed their mean time to decision—which means faster and more value from their data.

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