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.
Imply Polaris + Natural Intelligence: Powering Real-time Analytics & Observability
Imply Polaris provides the fastest path to real-time analytics by offering seamless data ingestion, auto-scaling capabilities, built-in visualization, and a secure, fully managed infrastructure. Watch this...
Watch nowPhysical Hardware, Digital Analytics: IoT Challenges, Best Practices, and Solutions
From supporting ad-hoc queries across massive time series datasets to ensuring data freshness and scalability, our panelists will share insights into selecting the right database solutions that can deliver...
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 now