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What are Events?

In the age of big data, events have become a critical source of information for businesses across a wide range of industries. Events refer to any occurrence or change in the state of a system that is worth recording and can provide valuable insights and data for analysis and decision-making.

Event processing has become increasingly important in the world of big data, and two critical technologies have emerged to enable businesses to derive insights from events: event stream processing and complex event processing.

Event stream processing

Event stream processing refers to the processing of continuous streams of data from various sources, such as sensors, social media, and other devices, in real-time. This technology enables businesses to derive insights from data as it's generated, allowing for more informed decision-making and faster response times.

Complex event processing

Complex event processing, on the other hand, refers to the processing of multiple events over time to identify patterns and trends that might not be apparent in individual events. This technology enables businesses to detect and respond to complex situations in real-time, such as fraud, network outages, or security breaches.

Event processing use cases in various industries

The importance of events and event processing can be seen in the various industries that have embraced these technologies. In finance, for example, event processing is critical for detecting fraud, monitoring market movements, and making informed investment decisions. Online retailers use event processing to improve customer engagement and increase sales by personalizing product recommendations, optimizing website navigation, or detecting and responding to cart abandonment. Travel companies rely heavily on event processing to provide personalized recommendations, optimize pricing, and provide real-time updates and alerts to customers.

Logistics and manufacturing companies use event processing to optimize supply chains, improve efficiency, and reduce costs by detecting and responding to delays, optimizing routes, and improving inventory management. Event processing is also becoming increasingly important in the gaming industry, where it's used to provide personalized experiences, optimize gameplay, and detect and prevent fraud.

OTT video streaming platforms use event processing to provide personalized recommendations, optimize content delivery, and detect and prevent piracy. In all of these industries, event processing is critical for deriving insights from data in real-time and making informed decisions that can drive business success.

Macrometa Stream Workers

Macrometa's Stream Workers provides typical streaming analytics capabilities, including joining streams, aggregations, counts, and filtering as well as sophisticated high-level primitives for complex event processing such as pattern recognition, conditional logic, and flow control. The Stream Workers engine runs within the Global Data Mesh, allowing you to incorporate operational data-at-rest with streams of data from both Macrometa’s streaming collections and other event streams, such as from Apache Kafka topics. Stream Workers can write to data stores within the Global Data Mesh or stream results to other streaming frameworks like Kafka.


In conclusion, events and event processing have become essential in the age of big data. With the rise of IoT, 5G, and other emerging technologies, the number of events generated per minute is only going to increase, making event processing even more critical for businesses across all industries. Whether it's in finance, e-commerce, travel, hospitality, logistics, manufacturing, gaming, or OTT video streaming, event processing enables businesses to gain real-time insights from their data, make informed decisions, and stay ahead of the competition.


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