Machine learning models require thousands of data points to train models. This session will look at a number of mechanisms druid offers to speed up model training. The tuple sketch can be used to train models where many different metrics are required for training. The sessionization extension can be used to speed up training for use cases where the need is to forecast viewership or session experience. Druid’s ability to output metrics at different time granularities can be used to smoothen out time forecasts.
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Learn how a decoupled architecture for Splunk—powered by Imply Lumi and Federated Search—helps you keep more data searchable, reduce costs, and scale efficiently without changing existing Splunk workflows.
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Watch nowKeynote: Powering Event-Driven Data with Apache Druid
The distinction between OLTP and OLAP is becoming less relevant as data architectures shift toward entities and events. In this session, we’ll delve into how Apache Druid’s event-first approach synthesizes...
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