Build Streaming AI Applications

IMPORT datasqrl.example.clickstream.Click;  -- Import data
/* Find next page visits within 10 minutes */
VisitAfter := SELECT b.url AS beforeURL, a.url AS afterURL,
    a.timestamp AS timestamp
    FROM Click b JOIN Click a ON b.userid=a.userid AND
        b.timestamp < a.timestamp AND
        b.timestamp >= a.timestamp - INTERVAL 10 MINUTE;
/* Recommend pages that are frequently co-visited */
Recommendation := SELECT beforeURL AS url, afterURL AS rec,
    count(1) AS frequency FROM VisitAfter
    GROUP BY url, rec ORDER BY url ASC, frequency DESC;

Simplify AI

Using SQL to construct features, compute vector embeddings, apply machine learning models, or deploy deep-neural networks allows you to build production-grade AI applications quickly.

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How DataSQRL Works

Implement your data processing in SQL and define your data API in GraphQL.
DataSQRL compiles optimized data pipelines that are robust, scalable, and easy to maintain.

How DataSQRL Works Exactly

Vector Embedding

DataSQRL supports vector embeddings, vector similarity, and vector search to build custom semantic search engines and personalized user experiences.

Feature Construction

Save a lot of time by constructing features with the SQL you already know and feed them directly into your AI or ML model.

Complete Integration

DataSQRL compiles integrated AI applications that combine data ingestion, data transformation, feature construction, model application, and API serving in one data pipeline.

Why DataSQRL?

Most AI projects fail because productizing AI models requires complex data plumbing to compute real-time features on data streams. DataSQRL eliminates data plumbing allowing you to focus on data transformation and model deployment. Utilize the power of AI without getting bogged down in implementation details.

Let's Build AI Data Products

Saves You Time

DataSQRL allows you to focus on your data processing by eliminating the data plumbing that strangles your data pipeline implementation with busywork: data mapping, schema management, data modeling, error handling, data serving, API generation, and so on.

Easy to Use

Implement your data processing with the SQL you already know. DataSQRL allows you to focus on the "what" and worry less about the "how". Import your functions when SQL is not enough - DataSQRL makes custom code integration easy.

Fast & Efficient

DataSQRL builds efficient data pipelines that optimize data processing, partitioning, index selection, view materialization, denormalization, and scalability. There actually is some neat technology behind this buzzword bingo.