GUI tool for streaming data analytics processing adds support for Spark Streaming to its support for Apache Storm.

I'm a sucker for a tool that adds a layer of abstraction and corrals quality into one thing manageable and a lot of simple. As such, Impetus' StreamAnalytix product has been on my radar for a few time, StreamAnalytix allows you to build graphical data pipelines that mix the employment of electronic communication platforms like Apache Kafka, Big Data Streaming Analytics platforms like Apache Storm, and prognosticative analytics technologies, like R and SAS.

But this morning, Impetus is taking things a step more, asserting the discharge of StreamAnalytix 2.0, that adds support for Apache Spark Streaming. we have a tendency to area unit finally commencing to see the triumph of smart tools over platform in-fighting.

Easier assembly

StreamAnalytix already lets data developers integrate a streaming data engine with Apache Kafka and RabbitMQ as publish/subscribe message buses. It conjointly allows the integration of HDFS, Amazon S3, Apache HBase, Cassandra, Solr and ElasticSearch. All of this can be done through a mixture drag-and-drop visual programming associated an array of declarative functions that permit you are doing things sort of a code operation in HBase or Cassandra, or perhaps MySQL.

StreamAnalytix conjointly supports versioning; a SQL syntax for common CEP (complex event processing) tasks and therefore the ability to push real time analytics updates via WebSockets. It conjointly permits you to method streaming data analytics through your own Java functions (just offer StreamAnalytix a category, entry purpose and parameter info) and it'll beware of replicating your code across nodes in an exceedingly cluster and corporal punishment it in an exceedingly data processing configuration.

And if that were not enough, StreamAnalytix includes its own dashboard authoring tools that let you show of knowledge that updates and changes in real time analytics.

One, the other, or both?

Selecting Apache Storm or Spark in StreamAnalytix unveil a designer with each common and platform-specific functions to incorporate in your pipeline. Meaning a given stream process style is coupled to a particular engine. however since the pipelines themselves area unit just persisted as JSON files, StreamAnalytix may in the future even yield the conversion of pipelines from one streaming engine to subsequent.

Folks from Impetus aforementioned that whereas this is not on the roadmap in any official sense, that it is a situation they've thought of and one they see as a logical progression from wherever they're. For more information visit: www.streamanalytix.com/spark-streaming-for-real-time-analytics