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 entities from streams, playing a crucial role in modern, decomposed databases. We’ll explore real-world examples of how Druid helps teams navigate complex queries and evolving infrastructure. This discussion aims to share how Druid’s unique perspective on event-centric data querying is shaping the way we think about modern data systems.

Join us as we explore a pivotal year for both Apache Druid and Imply Polaris. This keynote will review 2024’s key advancements—from enhancing Druid’s flexibility in data ingestion and establishing SQL the standard query language, to the development of the Event DB, unlocking new possibilities for data analysis. On the Imply Polaris side, we’ll showcase its evolution into a robust, cloud-native platform for running Druid. Looking ahead to 2025, we’ll explore Druid’s continued innovation, supporting hot, warm, and cold queries across real-time and historical data, pushing the boundaries of price-performance, and integrating seamlessly into the broader data ecosystem.

Modern data architectures often combine Apache Druid, an open-source high performance datastore, with Apache Iceberg, an open-source format for huge analytic tables. The ingestion of Apache Iceberg tables into Apache Druid provides organizations with concurrent streaming analytics and fast data exploration.

As Apache Iceberg becomes the de facto open table format for analytical datasets, there is a growing need to have specific Apache Iceberg tables containing high cardinality, highly dimensional event data available for rapid and open-ended data exploration. This talk discusses how Druid addresses this need by extending its ingestion layer to read the Iceberg table format. This integration helps Druid power interactive dashboards and support slice-n-dice analytics within Data Lakehouses.

By the end of this session, participants will understand:
* How Iceberg tables can be ingested into Druid
* Real-world use cases

In this session we will go over the flow of building a natural language query translation service for SaaS distribution of Druid and handling the unique considerations for Druid and the SaaS platform.

Delta Lake is an open-source storage layer that brings reliability and performance to data lakes. In this session, we will explore the fundamentals of Delta Lake and demonstrate how to ingest Delta Lake tables into Apache Druid for sub-second query latency. Additionally, we’ll introduce a new upcoming feature in Apache Druid: scheduled batch supervisors, which can facilitate continuous data ingestion natively in Druid, ensuring that your Druid tables stay up-to-date with the latest data from Delta Lake or any input source.

Analytics applications powered by Apache Druid are fun places to be. Take a seat as user experience leaders from Google, the Data Visualisation Society, and Imply bring their minds together, and answer your questions on what users of the future want from their data experiences.

Apache Iceberg and Delta Lake are coming into the big top, and Apache Druid is ready! Get ready to hear from Apache Druid PMC members and industry experts on just where Apache Druid fits, how it can be used, and what’s in the crystal ball for Druid.

Branch.io has been leveraging Apache Druid for real-time analytics for over eight years, handling more than 600,000 queries daily. In this presentation, we will explore the diverse use cases that Druid addresses for Branch.io, detailing our data ingestion methods through both real-time and batch processes. We will discuss various optimizations implemented to enhance query performance and reduce costs, including techniques such as cardinality capping, data rollup to higher granularity, and multidimensional partitioning. Additionally, we’ll cover our packaging and deployment strategies, and conclude with insights into our approach to query debugging.

Imply Polaris is a cloud-native database-as-a-service that simplifies the developer experience by providing all the advantages of Druid, plus auto-scaling for seamless data ingestion, enhanced security, exclusive features, and an easy interface to visualize your data in real time. Leading companies like Cisco ThousandEyes, Nestle and Zillow Group have achieved better efficiencies at scale with Polaris.
In this session, we’ll provide an overview of how Polaris serves as the easy button for Druid – including an architectural overview, product differentiators, and top technical use cases such as asynchronous query, time series analysis and more.

As a data-driven company, Netflix continually seeks to enhance the performance and reliability of its data infrastructure. This talk will delve into our sophisticated approach to optimizing Apache Druid configurations through parallel runs and A/B testing methodologies. We will explore how Netflix tests various Druid setups by running them concurrently against dual systems, enabling a direct comparison of key performance metrics reported by different clusters. Attendees will gain insights into the following areas:
1. **Cluster Management and Deployment:** An overview of Netflix’s strategies for managing and deploying Druid clusters, emphasizing automation and scalability.
2. **Centralized Logging and Metrics:** Techniques for aggregating and analyzing logs and metrics to facilitate real-time monitoring and post-mortem analysis.
3. **Cluster Architecture Patterns:** Best practices and patterns employed by Netflix to architect Druid clusters for optimal performance and reliability.
4. **Parallel Testing Framework:** Detailed methodologies for executing parallel runs and conducting A/B testing to evaluate different Druid configurations, including the tools and frameworks used.

This session will provide practical knowledge and actionable insights, empowering attendees to apply similar strategies within their own organizations to optimize Druid deployments. Join us to learn how Netflix leverages advanced testing and analytical techniques to push the boundaries of what is possible with Apache Druid.

Join us for an in-depth look at Retailcloud’s groundbreaking Data and Analytics Platform (DAP), engineered to handle and analyze millions of transactions in real-time during major sporting events such as the NBA, NFL, and Golf tournaments. This innovative platform focuses on stadium merchandising operations, where the need for timely insights is paramount to optimizing sales performance. By harnessing the powerful capabilities of Confluent and Imply, we deliver real-time analytics that provide our customers with actionable insights into sales trends and performance metrics. This session will explore how these insights empower our clients to make swift, informed decisions, thereby maximizing their sales potential during high-profile events. Discover how Retailcloud is transforming the landscape of real-time analytics in stadium merchandising.