WebAction’s realtime stream analytics platform is built from the ground up to provide ease-of-use while implementing enterprise-grade scalability, flexibility and security. Enterprises can filter, correlate, transform, and enrich data across both streams and static data sets using a graphical user interface and a declarative SQL-like language.

Leveraging WebAction’s straightforward components, users can quickly deliver robust, realtime stream analytics solutions that are perfectly tailored to their business and information requirements.

WebAction Architecture

The WebAction platform is broken down into five logical components: Acquisition, Processing, Analytics & Apps, Delivery (alerting, visualization, and persistence), and an integrated development environment.

  • Continuous Data Acquisition: Pre-built connectors assimilate a wide variety of continuous data feeds and static data sets using a template-driven user interface
  • Continuous Data Processing: Distributed in-memory processing and streaming data management that integrates streams, windows, and caches, and assigns data types on the fly for robust applications and analytics
  • Continuous Analytics and Apps: Continuous Queries to simplify implementing complex business logic. Built-in functions range from simple filtering, correlation, aggregation, and thresholding, to more advanced functions such as predictive operations and importing custom-built or best-of-class java libraries such as Mahout
  • Continuous Data Delivery
    • Visualizations and Dashboards: Browser-based interface to build out a visualization canvas using drag-and-drop elements such as charts, graphs, maps, and grids, allowing users to quickly present data in realtime and support drill-downs
    • Alerting: Configurable alerts — using email, SMS, JMS, and other messaging frameworks — instantly deliver correlated events of interest to other applications and end users
    • Persistence and Workflow Integration: Big Data records that contain contextual information and associated events of interest can be persisted in-memory for immediate access as well as stored in relational databases and big data stores. Persisted data can be accessed through ODBC/JDBC interfaces and published REST APIs
  • Stream Analytics Development Environment: An integrated GUI and command line development environment for building stream analytics apps

WebAction-Architecture-Philosophy2

WebAction High-Velocity Big Data Stream Analytics Overview

A Better High-Velocity Stream Analytics Solution

Many companies come to WebAction after trying to build a custom stream analytics solution in-house, or after attempting to use a product that simply extends open-source protocols or Hadoop. They make the switch because WebAction:

  • Involves all necessary data (structured and unstructured, varied hierarchies, etc.) via easy-to-use adapters
  • Provides an integrated end-to-end solution — from data collection, to aggregation/correlation, to visualization — all in realtime, with no interference in production workflows
  • Correlates across streams, as well as streaming plus static data
  • Natively incorporates and provides easy access to best-of-breed open-source components
  • Enables streaming visualization via continuous data processing and windowing
  • Provides consistency of implementation