Stream Processing Challenges

Event stream processing is the perfect approach for a lightning-fast decision making

Event Stream Processing is constantly growing and, most importantly, is spreading on business cases previously only related to batch data processing. Event stream processing has been proving essential on three use cases categories:

Where performances and throughput are critical factors and decisions must be taken within milliseconds: Cybersecurity, Sports Telemetry, Fraud Detection, Safety, Intelligence

    Where computation on event-based data sources (data streams) is carrying a significant – or crucial – part of the value coming from the resulting analytics: IoT based data sources (Automotive, Industry 4.0, Home Automation applications), Mobile App, Clickstreams.

    Where the business case involve the transformation/aggregation/transfer of data residing in heterogeneous sources with serious constraints and limitations, such as mainframes and legacy systems:  Mainframe off-loading, supply chain integration, core business processes.

    IOT General Scenario

    Companies services are relying more and more on connected devices: the IoT related business cases are requiring to process events as fast as they come in,, getting results in near-real time. Streaming technology is the most natural choice in order to:

    • Gather data streams coming from connected devices
    • Transform & Enrich the data combining such streams with other internal sources (i.e. CRM data, Banking statements, etc)
    • Apply Machine learning techniques and A.I.
    • Publish the processed data to other systems
    • Produce Descriptive, Diagnostic, Predictive and Prescriptive analytics

    Data Integration

    Combining multiple and heterogeneous data sources is one of the most critical challenges for IT departments. Addressing the “offloading” use case is becoming a priority for companies willing to get the most from their data. A good offloading case will include:

    • Getting data out of legacy system in near real-time
    • Move data in a modern and efficient infrastructure
    • Process data in near real-time in order to enable several business scenarios (among them: customer 360, context aware promotions, supply chain optimization… etc)
    • Enable the application of machine learning techniques and A.I. within the data processing pipeline (e.g. predictive maintenance, smart grid, smart supply chain)


      Algorithmic Trading, Stock Market Surveillance

      Smart Patient Care

      Monitoring a production line

      Supply chain optimizations

      Intrusion, Surveillance and Fraud Detection optimizations

      Smart Device Applications: Smart Car, Smart Home

      Smart Grid 

      Traffic Monitoring, Geofencing, Vehicle, and Wildlife tracking 

      Sports analytics 

      Context-aware promotions and advertising

      Computer system and network monitoring

      Predictive Maintenance

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