Modern applications demand instant insights on constantly changing data — from fraud detection and IoT monitoring to real-time personalization and AI-driven decision making. However, traditional data warehouses built for batch processing often struggle to keep up with the speed, scale and concurrency required for these use cases.

In this session, TJ Gibson will walk through how to design a real-time data warehouse that can handle continuous data ingestion, low-latency processing and high-concurrency analytics. You’ll learn the core architectural principles behind real-time systems and how they enable organizations to act on data the moment it arrives.

We’ll also explore how modern approaches unify transactional and analytical workloads, making it possible to run complex queries on fresh data without relying on fragmented pipelines or delayed batch updates. Real-world examples will illustrate how teams are building systems that support everything from live dashboards to AI-powered applications.

In this webinar, you’ll learn how to:

•  Design systems for continuous data ingestion and real-time querying

•  Achieve low-latency analytics on high-volume data streams

•  Support high concurrency without sacrificing performance

•  Build scalable architectures for modern data and AI applications

•  Avoid common pitfalls of traditional batch-based data warehouses

This session is ideal for data engineers, architects and developers building applications that depend on real-time insights and operational intelligence.

Speakers