Impetus Technologies Announces StreamAnalytix 2.0 Featuring Support for Apache Spark

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Impetus_Technologies_LogoImpetus Technologies announced StreamAnalytix™ 2.0, featuring support for Apache Spark Streaming, in addition to the current support for Apache Storm. The platform will provide enterprises with the advantages of the industry’s first open-source based, enterprise-grade, multi-engine platform for rapid and easy development of real-time streaming analytics applications.

Among stream processing engines, Spark Streaming is gaining popularity, while Apache Storm has been in production deployments for many years and is a robust, proven, widely used option. StreamAnalytix 2.0 builds on its existing visual integrated development and application-monitoring environment to provide abstraction over multiple streaming engines. It can also accommodate newer engines as they gain market acceptance. This approach allows developers and data analysts to use drag-and-drop operators to create real-time analytics applications by choosing the most optimal engine for each use case.

Over the past 18 months, we have worked with a number of global enterprises to implement real-time decision support and analytics applications on high-throughput streaming data sources in the areas of customer analytics and operational intelligence. In many instances, we were seeing use cases that are best optimized by utilizing different stream processing paradigms,” said Anand Venugopal, head of product for StreamAnalytix at Impetus Technologies. “Specifically, some required the low-latency, event-level processing that only Apache Storm could address. In other cases, the micro-batch architecture of Apache Spark Streaming with its leverage of Spark SQL and MLlib for machine learning was the best fit. We are excited about offering a platform that now simplifies those tradeoffs by incorporating both technologies under one easy and uniform user experience.”

StreamAnalytix has helped organizations in areas such as Internet of Things (IoT), sensor data analytics, e-Commerce and Internet advertising, security, fraud, insurance claim validation, credit-line management, call center analytics, business activity monitoring, and log analytics.

Along with the early success of customers deploying real-time analytics solutions based on streaming data, we are seeing many new proprietary and open-source based, real-time streaming solutions hit the market,” said Les Yeamans, founder of “A solution like Impetus’ StreamAnalytix 2.0, which is architected to provide a level of abstraction that allows for deployment of multiple streaming engines depending on the use-case requirements, affords customers a new level of ‘best-of-breed’ flexibility in their real-time architecture.” 

StreamAnalytix 2.0 builds upon the successful adoption of version 1.0, which is used by leading Fortune 1000 companies that are taking advantage of streaming data for improved business outcomes. In addition to support for Spark Streaming, there are a number of important functional enhancements in this release, including:

Spark Streaming

  • Rich array of drag-and-drop Spark data transformations
  • Support for Spark SQL and MLlib operations

Platform Enhancements

  • Ability to interconnect subsystems, which individually use different streaming engines
  • Embedded complex event processing engine enhanced for high-availability support
  • Built-in operators for predictive models including inline model-test feature
  • Additional support for industry standard message queue systems, including Amazon Kinesis and Simple Storage Service (S3), Apache ActiveMQ, IBM MQ and TIBCO
  • Enhanced self-service, real-time dash-boarding with editable widgets for various chart types
  • Multi-tenancy controls with the ability to restrict resources for specific tenants and pipelines
  • Ability to create multiple versions of real-time pipelines and choose the active version
  • Rich array of real-time data processing functions for string, time, date, numeric and other data types
  • Code-free enrichment and blending of streaming data with static data with lookups and MVEL expressions
  • Extensibility of stream-processing operators and libraries with user-defined functions

StreamAnalytix 2.0 will be generally available by the end of March. For more information, visit: Enterprises interested in accessing StreamAnalytix 2.0 now are encouraged to apply for the beta program by sending a request to


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