“Stream processing is rapidly evolving to meet the high-demand, real-time requirements of today’s data-driven world. As organizations seek to leverage the real-time insights offered by streaming data, the need for robust, highly concurrent analytics platforms has never been greater.

This presentation introduces Apache Druid, a modern, open-source data store designed for such real-time analytical workloads. Apache Druid’s key strength lies in its ability to ingest massive quantities of event data and provide sub-second queries, making it a leading choice for high concurrency streaming analytics.

Our exploration will cover the architecture, its underlying principles, tuning principals and the unique features that make it optimal for high concurrency use-cases. We’ll dive into real-life applications, demonstrate how Druid addresses the challenge of immediate data visibility, and discuss its role in powering interactive, exploratory analytics on streaming data.

Participants will gain an in-depth understanding of Apache Druid’s value in the rapidly evolving landscape of streaming analytics and will be equipped with the knowledge to harness its power in their own data-intensive environments.

Join us as we delve into the future of real-time analytics, discovering how to ‘Shaping the Future: Unveiling High-Concurrency Streaming Analytics with Apache Druid’.

Speakers:

Tijo Thomas: Imply Data inc, Lead Solutions Architect , TijoThomas

Lead with great passion for big data technology, having 18+ years of experience in the software industry (engineering, professional service , product management). Helping customer in the field , negotiating with customer on the feature request and align them with the product roadmap Extensive experience across the stack in Managing, Architecting, Designing and Implementing Big data applications, frameworks and platforms. More than 4 year of experience as Solution Architect Experience in design and implementing a highly scalable SAAS platform for public Cloud. Hold two patents in the area of Big Data.