Dec 06, 2023

Learn Druid with learn-druid

In this session you will learn about what it takes to process joins in distributed databases and then we’ll take a deep dive into how Apache Druid does joins in each of its processing engines. Druid’s native query engine is designed for fast queries and it can do joins, we’ll talk about query design to take advantage of how it works. You will also learn its limits and when a use case is better resolved by the Multi-Stage Query engine (MSQ). We’ll review the current state of MSQ-based joins and when to use broadcast vs sortMerge join algorithms based on how each works and how they use resources.

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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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Strategies for Managing Your Splunk Spend at Scale in 2026

Learn how a decoupled architecture for Splunk—powered by Imply Lumi and Federated Search—helps you keep more data searchable, reduce costs, and scale efficiently without changing existing Splunk workflows.

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Observability at a Breaking Point: How Decoupling Unlocks Speed, Scale, & Savings

Learn how decoupled observability helps you do more with your Splunk data, reduce costs, and scale efficiently with Federated Search.

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