Impetus Technologies Announces StreamAnalytix 3.0 Featuring Support for Apache Spark-Based Batch Processing

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Impetus Technologies, a big data thought leader and software solutions company, announced StreamAnalytix™ 3.0 featuring support for Apache Spark-based batch processing and enriched online and offline machine learning features, helping enterprises maximize the performance of their analytical models and achieve the most favorable business outcomes. The newest version adds to the stream processing capabilities driven by Apache Spark Streaming and Apache Storm so that data and analytics professionals can now orchestrate and visualize streaming and batch workflows in one unified, powerful platform that enables real-time data ingestion, processing and advanced analytics.

Spark Streaming has rapidly gained popularity as one of the most widely used platforms to process streaming data; however, most enterprise big data use cases today need both Spark Streaming and Spark batch,” said Anand Venugopal, head of StreamAnalytix at Impetus Technologies. “Based on strong market demand, StreamAnalytix 3.0 is now able to process Spark Streaming, Spark batch and even interconnected workflows. The new debug features for development time and run time in this release are also very sought after by our Spark customers. Overall, these new abilities give enterprises convenient access to a single visual platform for analyzing both fast data and big data to deliver context-aware customer experiences, accelerate data-driven business processes and maximize operational efficiencies with real-time insights.”

Used by leading Fortune 1000 companies, StreamAnalytix is the industry’s first open-source based, enterprise-grade, multi-engine platform for rapid and easy development of real-time stream processing and machine learning applications. Business analysts, data scientists and developers in many large enterprises have found it surprisingly easy to develop complex high-powered big data analytics applications in a matter of days using StreamAnalytix. Users are leveraging the intuitive drag-and-drop user interface which abstracts a powerful open source stack including Apache Kafka, Apache Spark, Apache Storm, Hadoop and NoSQL data stores. StreamAnalytix is available on premise or in the cloud and helps organizations in areas such as streaming ETL, Internet of Things (IoT) and log analytics, real-time smart customer care and churn analytics, real-time fraud and anomaly detection, and predictive maintenance.

In addition to support for batch processing, which provides the ability to schedule batch jobs via Apache Oozie, StreamAnalytix 3.0 includes advanced machine learning capabilities such as support for A-B testing and the champion-challenger model framework. The platform now also includes the following enhancements:

  • Data inspect feature that allows testing and visualizing the data and logic flow in the pipeline during the development stage without using cluster resources. No other visual solution on the market today offers this capability with Apache Spark.
  • Data lineage feature that enables visualizing data flow in running pipelines for streaming or batch data. It can also be used to determine cumulative processing time lag at each component, data comparison between components and data at each processor.
  • Hadoop certification with Cloudera, in addition to Hortonworks and MapR.
  • New data connectors for IoT, social media and clickstream, in addition to Amazon Web Services Kinesis, Amazon Web Services Simple Storage Service and TIBCO Enterprise Service Bus.
  • Improved usability, including new, user interface validations and dashboards.

StreamAnalytix 3.0 will be available under a beta program online by the end of April 2017.


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