Compaction in the Druid database has been evolving into use cases beyond improving query performance and reducing storage. This talk will first go over what is compaction, current use cases for compaction, a beginner’s guide on setting up compaction (and automatic compaction), and common pitfalls when setting up auto-compaction. The talk will then drive into short and long term plans for compaction in Druid. Future features include creating derived datasources / materialized views and automatically changing or maintaining different schemas for the same datasource based on the age of data.
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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