Model Data in Weaviate

Overview

Model Data in Weaviate is an intermediate, project-based course for developers and data professionals who want to unlock the full potential of vector search. In modern AI applications, search speed and relevance are everything, and they both begin with a powerful data model. This course teaches you the principles of designing a sophisticated, high-performance schema, moving beyond flat data structures to build a connected graph of information. You will learn to model complex relationships using multi-class schemas and relational links. Through a hands-on project using Weaviate as our implementation tool, you will design a schema for a real-world dataset, import interconnected data objects, and, most importantly, learn how to benchmark your design choices. The course culminates in an evaluation where you will use query latency data to prove the performance gains of your schema. You’ll leave with not just a working project, but also a repeatable methodology for optimizing data architecture in any advanced search application.

Syllabus

Designing and Implementing a Relational Vector Schema

Good performance starts with good design. In this module, you will learn why a well-planned data model is the backbone of any scalable vector application. You'll go from foundational principles to hands-on implementation, creating a complete, multi-class schema with relational links in Weaviate.

Benchmarking and Optimizing Schema Performance

A schema isn't complete until you've proven its worth. In this final module, you will learn the professional skill of quantitative evaluation. You will benchmark your schema's query latency and analyze the results to justify why a relational model is superior for complex queries.