A docker compose datasqrl profile

This data package is used by the Finance Creditcard Chatbot example which demonstrates how DataSQRL enables real-time analytics and a rewards program based on credit card transactions. It showcases how to aggregate, enrich, and process streaming data to provide insights into customer spending and credit card rewards, all within a compact data pipeline.

This data package is used by the Healthcare Study Monitoring Example which demonstrates DataSQRL's capabilities in creating a data pipeline to monitor and analyze healthcare study data. We will create a pipeline that ingests raw data from clinical studies and exposes the data through a GraphQL API. The pipeline will combine study metadata, patient information, and sensor readings to provide insights into the studies.

This example showcases DataSQRL's ability to create a real-time, streaming data pipeline for tracking shipment deliveries in Manhattan. It demonstrates how to efficiently monitor shipments in transit, manage delivery data, and answer key queries like the number of shipments per customer and their current locations, all with minimal SQL code. This example highlights DataSQRL's power in simplifying complex, real-time data workflows for logistics and other similar use cases.

The com.datasqrl.math package offers a set of advanced mathematical functions designed for the SQRL framework, enabling efficient computations such as cube roots, arc tangents, and more.

This data package is used by the Customer 360 (Retail) Example which demonstrates how to integrate data from multiple sources to create a comprehensive Customer 360 view. By combining customer data with orders and product information, we can offer customers a detailed look at their purchase history and enable analytics and search functionalities through a GraphQL API.

This data package is used by the Sensors (Getting Started) example which demonstrates DataSQRL's capabilities by building a sensor metrics data pipeline. It serves as an ideal use case for real-time data aggregation, showcasing how sensor readings can be processed to monitor environmental changes such as temperature and humidity.

A sensor table package for the datasqrl quickstart https://www.datasqrl.com/docs/getting-started/quickstart/

A redpanda datasqrl profile