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.
Physical Hardware, Digital Analytics: IoT Challenges, Best Practices, and Solutions
Electric vehicle maker Rivian and German startup Thing-it were kind enough to talk us through how real-time data and analytics play a key part in the evolving landscape of IoT (Internet of Things). The wealth...
Watch nowAnalyzing streaming data with Apache Druid
Streaming data is not only data in motion—it’s a potential source of valuable insights, ready to be harvested and utilized. The challenge is to analyze streaming data at scale and extract these insights—before...
Watch nowReal-Time Analytics in the Real World
Engineering teams increasingly have to deliver insights in real-time. But as they aim to reduce latency from event-to-insight, they also face the challenge of dealing with larger and more complex data and concurrent...
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