Yes, AI databases can store both structured and unstructured data. These systems are designed to handle diverse data types, enabling developers to work with traditional tabular data (structured) as well as raw text, images, or sensor logs (unstructured) in a single environment. This flexibility is critical for AI applications, which often need to process mixed data sources to train models or power real-time decisions. For example, a recommendation system might use structured user profiles alongside unstructured product reviews to generate personalized suggestions.
Structured data refers to information organized into predefined formats, like databases with tables, columns, and rows. A classic example is a SQL database storing user IDs, purchase dates, and product categories. AI databases can manage this data using standard query languages (e.g., SQL) while integrating features like indexing and transactions. However, they also extend beyond traditional systems to handle unstructured data, such as social media posts, PDF documents, or video clips. For instance, a medical AI application might store patient records (structured) alongside MRI images (unstructured) in the same database, allowing unified access for analysis. Technologies like hybrid storage engines or document-oriented NoSQL components within the database enable this by separating storage layers or using flexible schemas.
The mechanics of handling mixed data depend on the database architecture. Some systems use dual storage engines: one optimized for structured queries and another for unstructured blobs. For example, PostgreSQL with its JSONB column type allows semi-structured data storage alongside traditional rows. More advanced AI databases might employ vector indexing for unstructured data, transforming images or text into numerical embeddings for similarity searches. Meanwhile, metadata like timestamps or labels (structured) can be attached to these vectors for hybrid queries—such as “find images labeled ‘cat’ created after 2022.” Tools like Milvus or Weaviate exemplify this approach by combining vector search with structured filtering. This dual capability reduces the need for separate systems, streamlining development and improving performance when processing AI workloads that rely on diverse data inputs.