Unravel Data Introduces Performance Management for Real-time Streaming Applications

MENLO PARK, Calif., Feb. 28, 2018 -- Unravel Data, the industry's only Application Performance Management (APM) platform designed for Big Data applications, today introduced APM for streaming applications. This new feature enables enterprises to improve performance and reliability of their Internet of Things (IoT), real-time, and other streaming applications. Fortune 500 companies rely heavily on real-time applications to deliver up-to-the-second analytics and the best user experience - Unravel’s latest innovation helps these mission-critical applications maintain peak performance.

New platform capabilities give enterprises better performance and reliability for critical real-time streaming applications running on Spark, Kafka and HBase

We are witnessing a massive growth in the number of enterprise applications that process data in a real-time and streaming fashion as it arrives. Stream processing powers some of the most critical undertakings today such as algorithmic trading, autonomous cars, and health monitoring. These streaming applications process real-time data feeds from IoT devices, financial transactions, social media comments, or from database update events. Systems like Kafka, Spark Streaming, and HBase have emerged as critical components of the Big Data stack to support these applications. These systems provide a unified and high-performance architecture for processing real-time data feeds.  

While this architecture has made it easy to create streaming applications, guaranteeing the performance and reliability of these applications is extremely challenging. If a streaming application starts to lag behind in processing data in real-time, then diagnosing the root cause takes considerable time and effort with current tools. The root cause could be attributed to a number of intertwined factors, making it hard and time consuming to pinpoint the exact cause. For example, the root cause may be an application problem (e.g., poor data partitioning in Spark Streaming) or a system problem (e.g., suboptimal configuration of Kafka), or an infrastructure problem (e.g., resource contention in the cloud). Unravel can automatically pinpoint the cause of bottlenecks, slowdowns, and failures in streaming applications. Furthermore, Unravel provides automatic fixes for these issues, thereby reducing the amount of time and resources needed to firefight such ongoing problems.

“Unravel's latest release makes huge strides towards giving enterprises the best performance, predictability, and reliability for all their streaming applications,” said Dr. Shivnath Babu, Chief Technology Officer, Unravel Data. “Unravel leverages recent advances in machine learning and AI to automate root-cause analysis and resolution by applying these techniques to the full-stack monitoring data available for streaming applications. This monitoring data includes metrics and logs from applications, from systems like Kafka, Spark Streaming, and HBase, as well as from on-premises and cloud infrastructure.”

Unravel will unveil the streaming applications features at Strata Data Conference San Jose on March 6th. The company will also explore the new capabilities further during an upcoming webinar.

About Unravel Data
Unravel Data provides the only Application Performance Management (APM) solution for Big Data. Unravel doesn't just monitor and unify system-level data, but rather tracks, correlates, and interprets performance data across the full-stack in-order to optimize, troubleshoot, and analyze from a single pane. Customers include leading Big Data practitioners such as Kaiser Permanente, Leidos, Autodesk and YP.com. Unravel Data was founded by Kunal Agarwal and Dr. Shivnath Babu when they experienced the frustration of manually troubleshooting performance problems in Big Data stacks firsthand. Unravel's founding team includes Big Data experts from companies such as Cloudera, Oracle, and Microsoft. Unravel Data has raised a total of $23M in three rounds of funding from Menlo Ventures, GGV Capital, Microsoft Ventures, Data Elite Ventures and Jyoti Bansal.

A photo accompanying this announcement is available at http://www.globenewswire.com/NewsRoom/AttachmentNg/5e199ec1-5194-45d2-ac3e-45363844172e

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