Stream Processing for Real-Time Analytics and Dashboards

As businesses continue to gather more real-time data about their customers, suppliers, systems, and smart devices, the ability to analyze these data sets in real time provides businesses with new capabilities to improve customer satisfaction and become more operationally efficient. Capturing, transporting, and processing event streams is one of the fastest growing big data and Internet of Things (IoT) use cases for organizations across industries. The Stream Processing Quick Start Solution will enable application developers and analytics teams to accelerate deployment of real-time stream analytics and dashboards.

Key business benefits include:

  • Detect abnormal behavior: Uncover difficult-to-detect abnormalities faster.
  • Instant customer insights: React to a customer’s action instantly and tie that action to historical trends.
  • Analytics at IoT scale: Capture and process large-scale event streams in real time, including from IoT data sources like sensor networks and smart devices.

What's Included?



Trial subscription of MapR Converged Data Platform Enterprise Premier for the duration of the quick-start.

professional services

Professional Services

3-10 weeks of engagement with MapR Professional Services Engineers and Data Scientists (Duration varies based upon the particular quick start.)



2 Academy Pro Subscriptions including Certification Exams.

The solution template in the Stream Processing Quick Start Solution includes real-time data workflows for event stream producers and consumers, statistical aggregations within the stream processing engine, and a search-based visualization interface to gain insights into outliers and trends. Installation and configuration of the MapR cluster is included within the scope of this Quick Start Solution.

Key solution capabilities

  • Real-time Stream Data Ingestion: Ingest large volumes of data in real time from a variety of data sources such as firewall logs, application logs, database change capture logs, and sensor/device data.
  • Real-time Stream Analytics: Using Spark Streaming, implement real-time stream analytics such as statistical aggregations based on business logic. Correlate incoming data streams against predefined thresholds to alert operational users.
  • Real-time Dashboards and Searchability: Build a framework to index results as well as raw data so end-users can easily find relevant data. Build dashboards that are constantly updated, delivering real-time insights into your business.

Streams Processing Template

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