With any open source technology that’s been around for a while, misconceptions and myths about what tech can or can’t do inevitably pop up. Sometimes there’s some truth but often, it’s either an outdated perception or sometimes, it’s just that you need to do things differently. Apache Druid is no exception.
Since Druid was designed for fast queries on large-scale datasets and primarily focuses on aggregating and filtering data across dimensions and measures, you’ve probably heard someone lament that it can’t do joins. Or maybe you’ve heard someone say that Druid isn’t great with updates. Or maybe you’ve even thought to yourself “Druid would be so much better if it was easier to set up.”
In this session, we’ll debunk those pesky Druid myths and talk about how Druid handles joins, updates, and more. We’ll also dive into where those ideas came from, what Druid is built for specifically, and invite you to look at Druid as not just another database.
Keynote: Powering Event-Driven Data with Apache Druid
The distinction between OLTP and OLAP is becoming less relevant as data architectures shift toward entities and events. In this session, we’ll delve into how Apache Druid’s event-first approach synthesizes...
Watch nowClosing Keynote: Charting the Future of Druid
What lies ahead for Apache Druid? Join us as we explore the evolving landscape of Druid’s query and storage engines, and how they are positioned to address the biggest challenges in event data for the future. Speaker: Gian...
Watch nowSalesforce: Tracing Service Dependencies at Scale with Druid and Flink
At Salesforce, we manage approximately 300 million distributed spans to infer service dependencies. We have successfully utilized a combination of Druid and Flink to handle this scale with high availability....
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