Chapter 5: Storing Vectors in ScyllaDB: The Vector Data Type

Tue, 17 Feb 2026 00:00:00 +0000

Chapter 5: Storing Vectors in ScyllaDB: The Vector Data Type

Welcome back, aspiring vector search expert! In the previous chapters, we laid the groundwork by understanding what vector embeddings are and how USearch helps us find similar vectors efficiently. Now, it’s time to bridge that knowledge with a robust, scalable database solution: ScyllaDB.

This chapter will guide you through the exciting world of storing your precious vector embeddings directly within ScyllaDB. You’ll learn about ScyllaDB’s native VECTOR data type, how to define it in your table schemas, and the fundamental steps to insert and retrieve vector data. This is a crucial step towards building real-time AI applications, as ScyllaDB’s Vector Search, generally available as of January 20, 2026, leverages USearch under the hood to provide massive-scale, low-latency vector capabilities.

Chapter 6: Performing Similarity Search Directly in ScyllaDB

Tue, 17 Feb 2026 00:00:00 +0000

Chapter 6: Performing Similarity Search Directly in ScyllaDB

Introduction

Welcome back, future vector search expert! In previous chapters, we explored the standalone power of USearch, learned how to create and query vector indexes, and understood the fundamental concepts behind vector embeddings. Now, it’s time to bring that power directly into your database.

This chapter is all about integrating vector search capabilities directly into ScyllaDB, a high-performance, real-time NoSQL database. ScyllaDB has embraced the growing need for AI-native applications by offering native vector search, leveraging USearch under the hood for its efficient Approximate Nearest Neighbor (ANN) indexing. This means you can store your data and its associated vector embeddings together and perform similarity queries without needing a separate vector database or complex synchronization. Pretty neat, right?

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Chapter 5: Storing Vectors in ScyllaDB: The Vector Data Type