Cassandra Data Modeling: Advanced Query Patterns
Learn when to leverage SAI instead of traditional denormalization techniques and how to implement vector search for content recommendations.
In this second session of our Cassandra data modeling series, we dive into the powerful Storage-Attached Indexes (SAI) introduced in Cassandra 5.0. Building on the foundational concepts from Session 1, we'll explore how these new indexing capabilities can transform your query patterns while continuing to use our KillrVideo application as a practical example. Learn when to leverage SAI instead of traditional denormalization techniques and how to implement vector search for content recommendations.
You'll learn:
- SAI Architecture and Performance: Understand the inner workings of Storage-Attached Indexes and how they differ from previous secondary index implementations
- Index vs. Denormalization Decision Framework: Develop clear criteria for choosing between SAI and traditional denormalization based on query patterns and performance requirements
- Vector Search Implementation: Master the practical implementation of vector search for content recommendations using the new vector type and ANN queries
- SAI Best Practices: Apply industry best practices for creating, managing, and optimizing SAI indexes in production environments
- KillrVideo Search and Discovery: Implement efficient video search, content tagging, and recommendation patterns using modern Cassandra 5.0 capabilities
Speakers
Patrick McFadin
Principal Technical Strategist & Cassandra Committer
DataStax
Livestream Resources
Session 1 - Foundations of Cassandra Data ModelingSession 2 - Advanced Query PatternsSession 3 - Data Types and Enhanced Functions for Cassandra Data ModelsSession 4 - Data Protection and Governance for Cassandra DataSession 5 - Data Model Migration Strategies - Cassandra 3.x to 5.0Interested in a Free Cassandra Health Check?
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