Himanshu Raja

Data leaders frequently face a challenging architectural decision: when deploying customer-facing dashboards or embedding analytics into applications, they often spin up a separate real-time serving layer to handle low latency, high concurrency workloads. While this can address performance, it introduces significant complexity, governance risks, and infrastructure overhead. This session challenges the necessity of that serving layer. We will explore how organizations can consolidate their infrastructure by serving high-concurrency end-user analytics directly from the lakehouse with Lakehouse//RT, through real-world customer case studies and benchmarks.

This session is repeated. If you are brand new to Databricks SQL and want to get a lightning tour of this intelligent data warehouse, this session is for you. Learn about the architecture of Databricks SQL. Then show how simple, streamlined interfaces are making it easier for analysts, developers, admins and business users to get their jobs done and questions answered. We’ll show how easy it is to create a warehouse, get data, transform it and build queries and dashboards. By the end of the session, you’ll be able to build a Databricks SQL warehouse in 5 minutes.

Learn about the most important and hottest new features of Amazon Redshift. In this session, we discuss the architectural evolution of Amazon Redshift and how it uses machine learning to create a self-optimizing data warehouse.