New Ryft Connector for Spark brings real-time performance, streamlined infrastructure and ultra-high efficiency to the Spark ecosystem to unlock insights from high-volume and velocity data

Rockville, MD — Nov. 17, 2015 — Ryft, the performance leader for data search and analysis, today announced the company debuted a native integration with Spark that brings the 100X power and performance of the Ryft ONE to Spark ecosystems while also streamlining the infrastructure required to extract insights from high-volume, velocity and variety big data. The Ryft Connector for Spark solves three of the biggest challenges facing customers trying to get faster insights from data: performance, complexity and efficiency.

By packing the power of an entire data center into an easy-to-use one-rack-unit-size device, the Ryft ONE streamlines and accelerates the analysis of both batch and streaming data. Ryft also drastically increases efficiency by using less power than a hair dryer to bring faster data analysis closer to the source of data, so organizations using Spark clusters can unlock greater value and insight from Internet of Things and other remote data sources.

While Spark is widely recognized as a better alternative to notoriously slow and complex Hadoop frameworks, it is still stifled by the bottlenecks inherent in x86-based hardware. The Ryft ONE and Ryft Connector for Spark were purpose-built as a heterogeneous computing architecture to eliminate these bottlenecks and deliver instant insights, which is why benchmarks show a single 1U Ryft ONE outperforms more than 100 Amazon Web Services (AWS) servers running Spark.

“Even with Spark, data analytics running on commodity clusters will never be fast and efficient enough to keep up with the needs of businesses. Enterprises have reached a tipping point in their quest for game-changing insights and are no longer willing to recklessly scale out to get the job done,” said Des Wilson, CEO of Ryft. “The Ryft ONE does what no other systems can do: condense and accelerate real-time analysis of any type or format of raw data by 100X using a powerful breakthrough in heterogeneous computing to achieve massively parallel performance, efficiency AND ease of use. The new Spark connector eliminates the current limitations on Spark to deliver unprecedented speed and actionable intelligence, so organizations can succeed with data-driven decisions.”

The lightning-fast and easy-to-use Ryft ONE data analytics engine with Spark integration:

  • Accelerates complex analytics by 100 to 200X by routing core analytical functions, such as search and fuzzy search, to the Ryft ONE.
  • Slashes the complexity, footprint and expense of big data infrastructures by pacing the power of a data center into a 1U device, eliminating the need to scale out to sprawling clusters.
  • Easily integrates Spark and Ryft functionality and advanced performance into existing algorithm sets and data analytics systems.
  • Accesses Ryft ONE functions as both Spark RDDs and DataFrames, giving users options to use either type for SparkContext.
  • Supports the preferred node configuration, which allows Spark to request that specific jobs, such as critical search and fuzzy search analytics, go to the Ryft ONE, and other less time-sensitive jobs go to other Spark workers.
  • Allows users to continue programming in Spark’s high-level languages (Java, Python, Scala and R).

About Ryft Systems, Inc.:
Ryft is the performance leader in high-speed data analytics solutions that accelerate and streamline terabyte- to petabyte-scale data analytics ecosystems by 100X. With more than a decade of experience delivering incredibly fast data analysis solutions to government agencies, Ryft is the only company that understands how to effectively apply the combination of FPGA compute acceleration and x86 integration to a broad set of data-intensive workloads from the Internet of Things, video cameras, web logs, customer data and other sources. Industry heavyweights in retail, finance, defense and healthcare trust Ryft to power a range of real-time intelligence applications.

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