I’ve always thought that an intricate understanding of how something works helps you use it more effectively. In this session, I’ll try to convey what I’ve learned from some of Apache Druid’s creators about the inner workings of streaming ingestion in Apache Druid . We’ll start at the high level with how parallelism throughout the data pipeline drives scalability for real-time analytics. Then we’ll open the hood and see what’s inside each parallel ingestion task. We’ll talk threads, buffers, inner architecture and some of the main configuration parameters that control both its ability to ingest streaming data and process queries on arrival.
Imply Lumi Observability Warehouse Demo
In this 30-minute session, you'll see a demo of Imply Lumi — the observability data layer built to help you store more, search faster, and reduce cost without changing your existing tools.
Watch nowLunch & Learn: Imply Lumi Observability Warehouse Demo
In this 30-minute session, you'll see a demo of Imply Lumi — the observability data layer built to help you store more, search faster, and reduce cost without changing your existing tools.
Watch nowImply Lumi: What’s New, What’s Next — and How to Unlock More Observability Value Today
Observability teams must retain more data, investigate faster, and control costs without disrupting existing tools. This live Imply Lumi update shows new ingestion, retention, search, Splunk interoperability,...
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