Snowflake is the leading cloud data warehouse, serving as the central repository for data in the enterprise. Although it has exceeded performance of most warehouses, it still takes seconds to return queries since it uses a columnar format. For user-facing analytics, particularly when embedding dashboards in SaaS applications, the performance requirements are in milliseconds.

An indexing approach optimizes for the low-latency, high-concurrency queries needed for user-facing analytics. It allows you to deliver sub-second queries with lesser compute. This tech talk is a practical guide to accelerating performance by indexing Snowflake data using Rockset.

• Price-performance tradeoffs: which types of queries benefit from indexing, how to decide which datasets to index, how to measure your compute efficiency and query acceleration
• Architecture overview: the fundamental differences between Snowflake’s columnar storage and Rockset’s Converged Indexing, and how they work together for different use cases
• Hands on labs: how to setup continuous indexing using dbt, Snowflake and Rockset
• Ritual case study - how Ritual, the leading online vitamin brand uses Snowflake and Rockset for real-time analytics to personalize e-commerce experiences
• Hedge fund case study: how a top hedge fund uses Snowflake and Rockset for real-time analytics to make better investment decisions

Register to learn more about how to index Snowflake with Rockset to deliver sub-second queries at lower compute cost.