DataTorrent recently announced the availability of DataTorrent Real-Time Streaming (RTS) 2.0, which builds on its June release of the 1.0 version of by providing enhanced capabilities to run real-time analytics on streaming Big data sets. DataTorrent RTS 2.0 boasts the ability to ingest data from “any source, any scale and any location” by means of over 75 connectors that allow the platform to ingest varieties of structured and unstructured data. In addition, this release delivers over 450 Java operators that allow data scientists to perform queries and advanced analytics on Big datasets including predictive analytics, statistical analysis and pattern recognition. In a phone interview with John Fanelli, DataTorrent’s VP of Marketing, Cloud Computing Today learned that the platform has begun work on a Private Beta of a product, codenamed Project DaVinci, to streamline the design of applications via a visual interface that allows data scientists to graphically select data sources, analytic operators and their inter-relationship as depicted below:
As the graphic illustrates, DataTorrent Project DaVinci (Private Beta) delivers a unique visual interface for the design of applications that leverage Hadoop-based datasets. Data scientists can take advantage of DataTorrent’s 450+ Java operators and the platform’s advanced analytics functionality to create and debug applications that utilize distributed datasets and streaming Big data. Meanwhile, DataTorrent RTS 2.0 also boasts the ability to store massive amounts of data in a “HDFS based distributed hash table” that facilitates rapid lookups of data for analytic purposes. With version 2.0, DataTorrent continues to disrupt the real-time, Big data analytics space by delivering a platform capable of ingesting data at any scale and running real-time analytics in the broader context of a seductive visual interface for creating Big data analytics applications. DataTorrent competes in the hotly contested real-time Big data analytics space alongside technologies such as Apache Spark, but delivers a range of functionality that supersedes Spark Streaming as illustrated by its application design, advanced analytics and flexible data ingestion capabilities.