1. Data Sheet
StreamAnalytix 2.0
Industry’s Only Multi-Engine Streaming
Analytics Platform
KEY FEATURES
• Easy drag-and drop UI
• Complex event processing
• Predictive Analytics and
Machine Learning
CLUSTER MANAGER
A web-based application
that creates, configures and
manages clusters of
StreamAnalytix. It also
provides graphical information
about the health of the cluster
and can configure alerts and
notifications
• Real-time Dashboards
StreamAnalytix 2.0 is architected to provide a level of abstraction that allows for
the deployment of multiple streaming engines depending on the use-case
requirements. This affords customers a new level of “best-of-breed” flexibility in their
real-time architecture.
With StreamAnalytix, you can use the visual IDE and an enhanced set of powerful
stream processing operators to easily construct data pipelines in a matter of minutes.
You can then deploy them to a stream processing engine of choice.
Enterprises are now rapidly moving to add real-time streaming analytics as a strategy for
becoming more agile and responsive to data available in real-time. StreamAnalytix is a
platform to build and deploy streaming analytics applications for any industry vertical, any
data format, and any use case.
Focus on your business logic. Leave the plumbing to StreamAnalytix
• Support for Spark Streaming
A rich array of drag-and-drop Spark data transformations including Machine Learning
operations to analyze data using SQL queries and save the query output in a data
store of choice. Built-in operators for predictive models with inline model-test feature
and graphs to visually analyze data for models like Neural Networks and Tree.
• Proven Open Source Stack
Ingest, store, and analyze millions of events per second with a pre-integrated package
of industry-preferred Open Source components: Hadoop, NoSQL, Kafka, RabbitMQ,
Apache Storm, Elastic Search, and Apache Solr.
• Visual Performance Monitoring
Monitor performance of running applications and their underlying compute components
visually through graphs. Set alerts to get real-time notification on threshold breaches.
• Rapid App Development
Integrate custom applications into the real-time data pipeline by visual drag and drop.
Rapidly port predictive analytics and machine learning models built in SAS or R via
PMML onto real-time data.
• Open, Flexible, & Extensible
Use any fast-ingest data store of your choice. Bring in any number of proprietary or
standard data sources. Integrate the real-time data pipeline with other existing
applications, based on configurable conditions.
