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October 5, 2020

Swim Delivers Business-Critical Insights and Responses – Continuously, in Context, at Scale.

Organizations are drowning in streaming data from their products, assets, cloud services, apps & infrastructure. Most of it is only ephemerally useful, but data streams never stop. How can these organizations deliver AI-driven insights and generate meaningful situational awareness to support business-critical decisions in real-time, when being stuck in batch-centric “store-then-analyze” architectures? Is it possible to analyze, learn and predict from live, streaming data on-the-fly, avoiding the complexities of model building, training and “ML-Ops”?

Organizations need to be able to analyze, learn and predict continuously, in context, at scale. They need to act in real-time or fall behind the rate at which their assets and infrastructure stream events. Continuous intelligence from Swim addresses the need to statefully fuse streaming and traditional data, analyzing, learning, and predicting on-the-fly in response to streaming data from distributed sources – concurrently and at huge scale. Which provides Swim customers with an opportunity to develop a significant competitive advantage in their markets.

Figure 1: Example of an asset and equipment efficiency, continuous intelligence application (source: Swim)

Continuous intelligence embraces infrastructure service patterns like “pub/sub” from event streaming. It addresses the application platform need to help organizations develop, deploy, and operate stateful applications that consume streaming events – analyzing, learning and predicting on the fly to deliver streams of real-time insights and responses.

Modern databases can store data for later analysis, and update relational tables or modify graphs, but they cannot understand the meaning of data, or deliver real-time, situationally relevant responses. Applications interpret events from the real-world to change a model of the state of the system, but a single event may cause state changes to multiple related entities. There’s another reason that smarter databases aren’t enough for modern applications – they don’t drive computation of insights or “push” them to users. Users want to deliver real-time responses to state changes as they occur, concurrently for all entities in the system. They want the application to always have an answer.

Continuous intelligence drives analysis from the arrival of data – adopting an “analyze-then-store” architecture that automatically builds and continuously executes a distributed, live model from streaming data. Whereas streaming analytics applications use a top-down query/response visualization/user-driven control loop, continuous intelligence applications continuously compute and stream insights, deliver truly real-time user experiences and facilitate real-time automatic responses at massive scale.

By offering Swim Continuum, the first open core enterprise platform for building and running continuous intelligence applications on-premises, in the cloud or at the edge, Swim re-imagines the entire software stack to solve problems that require data-driven applications to deliver real-time responses – continuously, in context and at scale. Swim Continuum provides businesses with complete situational awareness and operational decision support at every moment. Built upon the open source SwimOS core, Swim Continuum provides unprecedented performance and efficiency for operationalizing high-frequency, contextual data analytics and real-time visualizations of massive amounts of streaming and batch data. Its single, production-ready platform monitors and manages all Swim operations, creates engaging, connected user experiences and seamlessly interoperates with existing enterprise systems.

Figure 2: Swim Continuum – An open core, enterprise platform for continuous intelligence applications (source: Swim)

To learn more how to design modern, continuous intelligence solutions, download this new eBook from Swim. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data.

 

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