SwimOS

Applications that deliver continuous intelligence from streaming data must analyze a boundless stream of updates that can’t be stored or delayed, even during a network partition.  The “fire and forget” nature of real-world events means updates could be lost, causing apps to fall out of sync and making automation risky.  

Recently several developers have asked me to compare Swim (OSS SwimOS) with other well known Actor Systems and especially Akka, which is a widely used Actor based language.

How can you derive continuously useful intelligence from your event streams to help automate business decisions? This blog provides a worked example using streaming events from Apache Kafka.

Mark Burgess, of CFEngine and Promise Theory fame, gave this fabulous concluding talk at DevOpsDays Oslo two years ago. He observed that hoping that a complex system is in a given state is a belief that results from a failure to observe the truth and deduce the (complex) consequences fast enough to be able to be sure what happened.   

At Swim, we’re continuing our journey to help organizations transform their business operations - continuously augmenting human decision-making, using the most accurate, relevant data possible from real-time and contextual data sources. ... [More] As the first provider of an open core, end-to-end platform that enables Continuous Intelligence at scale, we’re pleased to announce the latest release of our flagship product, Swim Continuum 4.0. [Less]

A Technical Blog Series for Data Architects This multi-part blog series is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and ... [More] 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. This blog series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today. [Less]

A Technical Blog Series for Data Architects This multi-part blog series is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and ... [More] 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. This blog series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today. [Less]

A Technical Blog Series for Data Architects This multi-part blog series is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and ... [More] 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. This blog series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today. [Less]

A Technical Blog Series for Data Architects This multi-part blog series is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and ... [More] 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. This blog series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today. [Less]

A Technical Blog Series for Data Architects This multi-part blog series is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and ... [More] 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. This blog series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today. [Less]