AI Features

CrateDB acts as a real-time feature store, managing the data that fuels your models, from telemetry and sensor readings to user behavior and system metrics. Features are aggregated, indexed, and made instantly available for training or inference.

Why it matters:

• Keep models synchronized with the latest state of the world
• Eliminate lag between ingestion and inference
• Avoid complex data pipelines and batch jobs

CrateDB integrates easily with stream processing frameworks like Apache Flink and machine learning platforms such as TensorFlow, PyTorch, or scikit-learn.
Data scientists can query features directly using SQL or connect CrateDB to their ML workflows for online and offline training.

Integration examples:

• Stream real-time telemetry into Flink for preprocessing
• Query recent feature vectors for model updates
• Use CrateDB as a high-performance feature source for deployed models

CrateDB natively supports vector data types and similarity search through the KNN_MATCH() function, allowing AI applications to perform semantic queries at scale. Whether you're building recommendation systems or intelligent search, CrateDB enables instant similarity lookups across millions of embeddings.

Use cases:

• Product and content recommendations
• Semantic document retrieval
• Anomaly detection using learned embeddings

By combining real-time ingestion, distributed aggregation, and hybrid search, CrateDB enables continuous intelligence across your operations. From predictive maintenance and fraud detection to personalized user experiences, your models always act on the latest data, not yesterday’s.

Real-world examples:

• Predict equipment failures based on live sensor data
• Detect anomalies in streaming financial transactions
• Serve AI-driven recommendations that adapt instantly