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Rise Of The Citizen Data Scientist

For decades, enterprise IT has been on a constant trend of taking responsibilities from business units into a central IT function who provide technology “as a service” to the rest of the organisation. This worked for a long time, when enterprise IT was all about cost control, stability, governance and managing outsourcers. In todays world

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Closed Loop Analytics

Many companies today are interested in using data to improve their business and customer experience.   Most of these initiatives ultimately leave a human in the loop, making decisions and implementing tactical or strategic business changes as a result of the reports, dashboards and analytics they are armed with: Having this human in the loop brings

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Analytics Need To Be Real Time To Maximise Their Value

Most analytics and business intelligence programmes target reports and dashboards as a means of communicating the insights to users. Users then need to login to access those reports which are perhaps updated at end of day. They then need to interpret the data and action the recommendations, perhaps communicating to other people in order to do so. This introduces a big delay from identifying the insight to having it actioned in the real world.

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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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