Imply Polaris offers Druid-as-a-Service to users with the ability to pay for ingestion only when it is used. To achieve this and keep costs down, Druid needs to scale its ingestion resources in response to customer workloads. In this session, we will talk about how we deployed Apache Druid to provide this capability to our customers first using Druid’s in built auto-scaling ingestion mechanics and then by switching to middle manager less ingestion using the druid-kubernetes-overlord extension and Kubernetes node scaling In the talk, we will discuss some of the key problems we had to solve to make deploying middle manager less ingestion a success like minimizing pod startup time and tradeoffs to consider before switching to middle manager less ingestion.
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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