AI databases store a wide variety of data types to support machine learning models, analytics, and real-time applications. The most common categories include structured data (e.g., tabular records), unstructured data (e.g., text, images, audio), time-series data (e.g., sensor readings), and graph data (e.g., social network connections). Additionally, metadata—such as annotations, labels, and data lineage—is critical for organizing and interpreting datasets. AI systems also rely on embeddings (numerical representations of data) generated by models like transformers or CNNs to enable efficient processing. The choice of data type depends on the use case, such as training models, serving predictions, or analyzing trends.
Structured data is foundational for many AI workflows. This includes CSV files, database tables, or formatted logs with clearly defined schemas. For example, a fraud detection system might store transaction records with fields like timestamp, amount, and user ID. Time-series data, often used in IoT or financial applications, adds a temporal dimension—like temperature measurements from sensors or stock prices over time. Semi-structured formats (e.g., JSON, XML) also fall into this category, blending flexibility with partial organization. Unstructured data, such as raw text from emails or social media posts, requires preprocessing (tokenization, encoding) before use in natural language processing (NLP) tasks. Similarly, image or video data might be stored as binary files with metadata describing resolution, format, or object annotations.
AI databases also handle specialized formats to optimize performance. Embeddings (dense vectors representing words, images, or user behavior) are a key example. These are generated by models like BERT or ResNet and stored for tasks like similarity search or recommendation systems. Graph databases excel at storing relationships—for instance, representing user interactions in a social network or fraud rings in a financial graph. Operational data, such as model versions, hyperparameters, and training logs, is often stored alongside raw datasets to track experiments and reproduce results. Lastly, synthetic data—artificially generated to augment training sets—is increasingly common, especially in domains where real data is scarce or sensitive. For example, GANs (Generative Adversarial Networks) can create realistic images for computer vision tasks. Scalability, latency, and compliance (e.g., GDPR, HIPAA) further influence how data is partitioned, encrypted, or cached in these systems.