On this episode, we explore how Atlassian leverages Apache Druid's capabilities to handle millions of daily events and empower users with intelligent data-driven features. We’re joined by Gautam Jethwani and Kasirajan Selladurai Selvakumari from the Confluence Big Data Platform Team who will talk through how they use Druid to power intelligent features, sub-second query latency, and complex ingestion tasks.

When it comes to fraud detection, initial detection is key, but so is the ability to quickly dissect and address the problem to minimize losses. This means access to real-time data is paramount. The only way to combat fraud in the digital age is to fight fire with fire…automation with automation. In this episode, we’re joined by Jaylyn Stoesz, Staff Data Engineer at Ibotta, a free cashback rewards platform, who walks us through Ibotta’s multifaceted approach to fraud detection that includes Apache Druid and gives us the full scoop on their use of Imply Polaris.

We’re back again with another Druid release! Here we are at Apache Druid 27.0, thanks to the dedication of the Druid Community. This release was made possible by over 350 commits & 46 contributors. Will Xu, Product Manager at Imply joins the show to discuss new features like Smart Segment Loading, a new mechanism for managing data files as the database scales, improvements to schema auto-discovery, and the long-awaited feature – querying from deep storage!

Real-time data has many applications but one place where it’s extremely valuable is with usage tracking, billing, and generating reports. Ensuring the freshness and availability of this data is not only essential for financial success but also for establishing a more challenging aspect—trust. That's precisely why Orb chose Apache Druid and Imply as the backbone of their advanced pricing platform. This platform encompasses invoicing, usage monitoring, and comprehensive reporting. On this episode, Kshitij Grover, co-founder and CTO at Orb, guides us through their innovative utilization of Druid, allowing their users to assess metrics across queries and beyond. He also gives some great advice for those just starting on their real-time data journey. Definitely worth a listen...or two!

We’re back again with another Druid release! Here we are at Apache Druid 27.0, thanks to the dedication of the Druid Community. This release was made possible by over 350 commits & 46 contributors. Will Xu, Product Manager at Imply joins the show to discuss new features like Smart Segment Loading, a new mechanism for managing data files as the database scales, improvements to schema auto-discovery, and the long-awaited feature – querying from deep storage!

Apache Kafka® is a streaming platform that can handle large-scale, real-time data streams reliably. It’s used for real-time data pipelines, event sourcing, log aggregation, stream processing, and building analytics applications. Apache® Druid is a database designed to provide fast, interactive, and scalable analytics on time-series and event-based data, empowering organizations to derive insights, monitor real-time metrics, and build analytics applications. Naturally, these two things just go together and are often both key parts of a company’s data architecture. Confluent is one of those companies. On this episode, Kai Waehner, Field CTO at Confluent walks us through how they use Kafka and Druid together, where Apache Flink fits into the mix and shares insights and trends from the world of data streaming.

Today's show is all about the world of big data and open source projects, and we've got a real gem to share with you—Voltron Data!  They're on a mission to revolutionize the data analytics industry through open standards. To unleash the untapped potential in data, Voltron Data uses cutting-edge tech and provides top-notch support services, with a special focus on Apache Arrow. This open-source framework lets you process data in both flat and hierarchical formats, all packed into a super-efficient columnar memory setup. And that's not all! Meet Ibis—an amazing framework that gives data analysts, scientists, and engineers the power to access their data with a user-friendly and engine-agnostic Python library. Excited to learn more? We've got Josh Patterson, the CEO of Voltron Data, here to give us all the details.

Who better to talk about the real-world usage of Apache Druid than Digital Turbine, a leading mobile growth and monetization platform? The folks at DT go way back with Druid. On this episode Lioz Nudel, Engineering Group Manager at Digital Turbine and Alon Edelman, Data Architect at Digital Turbine discuss how Druid has significantly improved their analytics infrastructure in terms of performance and scalability. We cover their journey from using MySQL to Druid, highlighting the scalability, performance, and agility that Druid offers and delve into specific use cases, such as analyzing massive amounts of data and managing cloud computing costs.

Whether you're a data engineer, data scientist, technology enthusiast, or just a person on the Internet, you’ve heard about ChatGPT. But did you know there are some great use cases for it that work with Apache Druid? Druid and ChatGPT are two cutting-edge technologies that are revolutionizing the world of real-time analytics and natural language processing. In this episode, we’re joined by Rick Jacobs, Senior Technical Evangelist at Imply, who will dive into the benefits of combining a trained NLP model with Apache Druid for sentiment analysis and how he did it with ChatGPT. We’ll also get into the broader applications of AI and how to use it responsibly, especially when you’re dealing with important datasets.

Deploying and configuring Apache Druid manually in a Kubernetes environment can be complex and time-consuming. But it doesn’t have to be. Enter Druid Operator, a tool specifically designed for managing Apache Druid deployments in a Kubernetes environment. Adheip Singh, founder of DataInfra and contributor to Druid Operator, walks us through the benefits, including managing upgrades and rollbacks of Druid clusters, scaling Druid clusters, adding more storage to underlying persistent volume claims and more.