Persisted Datasets and SQL

If extracting data from complex JSON structures is slowing down your queries, or if you find yourself hunting through multiple files just to update a single Group Type ID, you are likely facing a maintenance bottleneck.

This video demonstrates two powerful, field-tested strategies for leveraging Persisted Datasets directly within SQL to solve these problems. You will learn how to "pivot" cached JSON data into high-performance, tabular results using OPENJSON, and discover the "Rigging Pattern"—a design strategy developed at Triumph to centralize your configuration settings. Whether you are building complex reports or simply trying to keep your code clean, you will leave with a technique that makes your SQL faster and your maintenance easier.

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Attribute Values In SQL

Attribute Values in SQL

Simplify your SQL queries when working with attribute values.

Persisted Datasets and SQL

Persisted Datasets and SQL

The video explores the intersection of Persisted Datasets and SQL in Rock RMS, focusing on two primary use cases: improving performance when querying complex data structures and centralizing configuration management through the "Rigging Pattern."

Check In Best Practices & Design Strategy

Check-In Best Practices & Design Strategy

Overly complicated Rock Check-In configurations turn weekends into a frustrating bottleneck for staff, volunteers, and families. Watch this video to learn how you can apply best practices for a smooth, flexible, and manageable experience.

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