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Timeflow As A Stream Processing Engine For Apache Druid

Timeflow is a “stream processing” or “complex event processing” engine. The platform allows us to listen to sequences of events and identify and respond to patterns of interest as they happen on the streams. An example of complex event processing in a business scenario might be “inform us when a high value customer places three

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Stateless and Stateful Event Processing

In previous blog articles, we discussed the potential of event stream processing as a means of improving customer experience and business efficiency.  In this article, we wanted to look deeper at the type of analysis we might want to do on these event streams to bring the opportunity to life.  Imagine a customer lifecycle which

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Why We Use Apache Druid As Our Datastore

Why We Use Apache Druid As Our Datastore When designing Timeflow, we evaluated a number of data stores for our event storage and processing requirements.   Many offerings in the modern data market, for instance in the NoSQL and cloud space, have specific strengths but also specific tradeoffs which have to be considered carefully. For

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The Challenges Of Stream Processing Applications

The idea of processing a stream of events is a relatively simple one to understand. However, to deliver on this technically quickly gets quite complex to do well. If trying to do this from scratch, some of the key challenges you would likely find are:

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In Business, Everything Is An Event

To improve our business, we are usually looking to understand, react to, or enhance business events in some way. When we do this, we can create better customer experience, identify opportunities for revenue growth, and make real improvements in business efficiency.

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