In this talk, we’ll learn more about how Lyft builds data pipelines using Apache Druid, which is useful for several use cases including metrics tracking, model forecasting, and internal tools. We’ll also talk about the challenges we faced while setting up our real-time ingestion pipeline into Druid using Apache Flink and Kafka, and how we went about solving them.
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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