DataTorrent RTS 2.0 Now Available 

DataTorrent, Inc. today announced the availability of DataTorrent RTS 2.0. With today's release, enterprises can easily take advantage of real-time streaming apps. Now, enterprise developers and data scientists can easily create real-time streaming, big data analytics applications. Separately, the company announced the private beta of Project DaVinci and Project Michelangelo

Successful organizations are able to make data-driven decisions quickly. Increasingly, however, organizations are unable to ingest or process the massive amount of data that is being created around their business in a timely manner. In some cases, the sheer volume of data being generated leaves organizations unable to even take action until hours or days later, rendering the data far less valuable to the business.

According to Forrester Research, Inc., "Market data, clickstream, mobile devices, sensors and even good, old fashioned transactions may contain valuable, but perishable insights. Perishable because the insights are only valuable if you can detect and act on them in real time."

DataTorrent RTS 2.0 allows organizations to harness the full potential of real-time big data and take action by enabling faster data ingestion, data processing, and more timely data insights in a high performance, scalable, highly available manner. Select key features include:

• "Any data, any source, any scale and any location," data ingestion: With more than 75 data connectors, DataTorrent RTS 2.0 enables organizations to ingest structured and unstructured data. The platform supports SLA driven auto-partitioning and auto-scaling. Data can be ingested from edge locations as well as at the data center via a hub-n-spoke architecture.
• More than 450 Open source Java operators for advanced data analytics: DataTorrent RTS 2.0 increases the company's Malhar open source library to include more than 450 open source operators, including capabilities such as filtering, pattern matching, RapidMiner support and dimensional processing to simplify advanced data analytics.
• Hadoop-native Distributed HashTable (HDHT) DataTorrent RTS 2.0 allows applications to store high volume and structured and unstructured data in an HDFS based distributed hash table. Order is maintained and instant lookups are allowed for real-time analytics and time based pattern matching. Thus speeding application development and reducing the cost and complexity associated with using a third party database.
• Dynamic Application Modifications with no-downtime: DataTorrent RTS 2.0 enables changes to an application's window time (e.g. 5 minute rolling window to 10 minute rolling window), native A-B testing of business logic and insertion/deletion of business logic without requiring an application or system shutdown.
• Streaming app development, deployment, and management made simple: DataTorrent RTS simplifies the design and debugging of distributed, big data streaming applications. It utilizes a visual application designer, an application and data dashboard, debugging tools and the industry's first Hadoop 2.0 streaming application packaging technology.

"Enterprise adoption of streaming analytics has been stunted, as existing alternatives provide a limited set of pre-built advanced analytics modules, have proprietary or difficult to use development environments and are unable to ingest data from multiple sources at massive scale," said Phu Hoang, co-founder and CEO of DataTorrent. "With DataTorrent RTS 2.0, we have addressed these challenges to enable rapid enterprise deployment of real-time streaming big data applications."