AI integration (Andreas)

CrateDB is not just a real-time analytics database, it's a powerful platform to feed and interact with machine learning models, thanks to its ability to store, query, and transform structured, unstructured, and vectorized data at scale using standard SQL.

Whether you're training models, running batch or real-time inference, or integrating with AI pipelines, CrateDB offers:

High-ingestion performance for time-series or sensor data
Real-time queries across structured and semi-structured data
SQL-powered transformations and filtering
Native support for embeddings via FLOAT_VECTOR

For more details on how CrateDB handles similarity search and embeddings, see the Vector Search use case.

Why CrateDB for ML Use Cases?

Feature

Benefit

Real-time ingestion

Ingest millions of records per second from IoT, logs, or user behavior

Unified data

Mix structured, full-text, vector, and JSON data

FLOAT_VECTOR

Store and query high-dimensional embeddings

SQL transforms

Use SQL for preprocessing, feature extraction, and filtering

ML integration

Use CrateDB as a feature store or inference backend

Python & LangChain support

Easily connect to training and inference pipelines

Common Machine Learning Patterns

Feature Engineering

Use SQL to build features dynamically from raw data.

SELECT
  user_id,
  AVG(duration) AS avg_session,
  COUNT(DISTINCT page) AS page_diversity
FROM sessions
GROUP BY user_id;

Training Dataset Extraction

Efficiently extract and filter relevant training data.

SELECT *
FROM telemetry
WHERE temperature > 80
  AND error_code IS NOT NULL
  AND ts BETWEEN NOW() - INTERVAL '7 days' AND NOW();

Store Embeddings

Save vector representations for documents or entities.

CREATE TABLE article_embeddings (
  id UUID PRIMARY KEY,
  content TEXT,
  embedding FLOAT_VECTOR(384)
);

CrateDB supports high-dimensional vectors with FLOAT_VECTOR. To query these vectors for similarity-based inference, see Vector Search.

Use CrateDB as a Feature Store

Centralize your features and use them in production models.

SELECT *
FROM user_features
WHERE last_active > NOW() - INTERVAL '1 day';

When to Use CrateDB in ML Pipelines

Use Case

CrateDB Role

Feature Store

Store pre-computed features with SQL access

Real-Time Inference

Serve vector-based results with KNN_MATCH

Experimentation

Use SQL for fast slicing, filtering, and aggregations

Monitoring

Track model performance, drift, or input quality

Data Collection

Capture telemetry, events, logs, and raw user data

Architecture Examples

Model Training Pipeline

[ Ingestion (sensors, APIs) ]
           ↓
     [ CrateDB ]
  (Real-time data lake)
           ↓
[ Python / Spark / Pandas ]
  (Feature engineering, training)
           ↓
[ Model Registry / Serving ]

Real-Time Inference with Hybrid Queries

[ CrateDB ]
 - JSON filters
 - Full-text search
 - FLOAT_VECTOR support
         ↓
  [ SQL + KNN_MATCH ]
         ↓
[ Application Response ]

Performance Tips for ML Scenarios

Tip

Benefit

Use FLOAT_VECTOR for embeddings

Store and access high-dimensional vectors efficiently

Normalize vectors before inference

Improves ANN accuracy

Index only what you need

Reduces overhead

Use hybrid filters + vector search

Improves performance and precision

Train offline, infer online

CrateDB is ideal for live inference from pre-trained models

Ecosystem & Integration

Tool

Integration

Python

crate-python for pandas, scikit-learn

LangChain

Native CrateDB vector store

Jupyter

Ideal for experimentation, model development

OpenAI, Cohere, etc.

Store and search their embeddings via SQL

Kafka

Connect for real-time ingestion and prediction