The previous units introduced you to the fundamentals of Weaviate, its capabilities, and how to execute basic queries on existing data. Now that you're familiar with the various query types, it's time to explore how to populate your own Weaviate instance with data.
In this unit, we'll examine how to use Weaviate to effectively structure your data so that you can retrieve the right information the way you want. We'll delve into defining a schema for your data and importing data into Weaviate.
By the end of this unit, Weaviate's overall data architecture will start to become clearer in your mind. This will start to empower you to build a vector database that really suits your needs and goals.
Let's get started.
- (Required) A Python (3) environment with
- (Required) Complete 101A Weaviate Academy Preparation
- (Recommended) Complete Hello, Weaviate
- (Recommended) Complete Queries 1
What are these?
- Learning Goals describe the unit's key topics and ideas.
- Learning Outcomes on the other hand describe tangible skills that the learner should be able to demonstrate
Here, we will cover:Learning Goals
- How Weaviate organizes and stores data.
- An overview of indexes used in Weaviate.
- What a schema is, and how to define it.
- How to define classes and properties, including appropriate data types.
- How to populate Weaviate with data.
- Some best practices such as batch imports and additional properties.
By the time you are finished, you will be able to:Learning Outcomes
- Describe how the schema relates to organization and storage of data in Weaviate.
- Broadly describe the role of indexes in Weaviate.
- Understand how classes and properties represent your data.
- Create a schema to suit your data.
- Populate Weaviate with data, using batch imports.