Table Partitioning Demo

This demo walks you through the new Table Partitioning feature in IRIS SQL, explaining what it does and how it works along the way. We'll only use a few dozen rows to prove the concept, but obviously the capability is focused on datasets many orders of magnitude larger.

If you'd like a shorter, higher-level introduction to Table Partitioning, check out the online learning module Managing Tables with Partitioning in InterSystems IRIS

ℹ️ Table Partitioning is included in IRIS 2026.1 as an experimental feature.

What is Table Partitioning?

Introduction
InterSystems IRIS Adaptive Analytics is an optional extension that provides a business-oriented, virtual data model layer between InterSystems IRIS and popular Business Intelligence (BI) and Artificial Intelligence (AI) client tools. Adaptive Analytics is powered by AtScale, the AtScale documentation can be found at this link: https://documentation.intersystems.atscale.com

This article will showcase some AtScale features that can facilitate data analysis:

1. Cube creation
2. Excel visualization
3. Parallel Period
4. Queries
5. Snowflake
6. Security

7. Aggregates

Hi, every one. Again, it's me!!😀😀

Recently I was trying to organize some learning materials for InterSystems IRIS😆, and realized that the resources are actually quite scattered.🤐

So I put together a list here—grouped by categories—for anyone who:

• is new to InterSystems
• or wants to go deeper into specific areas

Also adding some of my own experience on what worked (and what didn’t, may be only not work for me🤫🤐).

1. For starters

If you don’t know where to begin, start here:

• https://developer.intersystems.com/
• https://developer.intersystems.com/resources/
• https://developer.

Hi Community,

In our two preceding articles, we explored the fundamentals of the Interoperability Embedded Python (IoP) framework, including message handling, production setup, and Python-based business components.

In this third piece, we will examine advanced methodologies and practical patterns within the IoP framework that are pivotal for real-world interoperability implementations.
We will explore the following topics: 

✅ DTL (Data Transformation Language) in IoP
✅ JSON Schema Support
✅ Effective Debugging Techniques

Collectively, these features help us create maintainable, validated, and easily troubleshootable production-grade interoperability solutions.

Claude Code has a strong understanding of IRIS, but unexpected issues still occur.

The first issue is one that has already happened several times and is likely to continue occurring if not properly addressed.

In IRIS, the collation for string data (%String) is set to SQLUPPER by default. As a result, when data is retrieved via SQL, it may be returned in uppercase (for example, when sorting and aggregating with GROUP BY).

Since I started using Claude Code, my motivation to create things has skyrocketed.

Previously, even if I wanted to build something, actually doing the coding felt like a hassle, so unless there was a very strong need, I rarely went as far as programming. But now, if I just jot down the specifications, Claude Code handles the rest automatically, resulting in a dramatic improvement in productivity.

I come from a generation native to ObjectScript, so I used to feel some hesitation when it came to switching to Python.

This time, it’s not really programming per se, but we are using React as a front-end development tool for IRIS. When using a web development framework—not limited to React—one key consideration is which CSS framework to use. Up until now, we’ve been using Bootstrap, which is the standard and the easiest to get started with.

However, while it’s easy to use, I felt that it offers limited flexibility for customization. That said, even if we were to switch to another CSS framework, learning it and rewriting everything from scratch would be a significant effort, so we left things as they were.

Is your source control treating your InterSystems IRIS environment like a simple collection of files? Most source control tools lack the native intelligence to handle InterSystems IRIS, which is why your code is treated as such. 

Deltanji is different. It understands how code and other artifacts are stored within IRIS and can manage them directly. This eliminates the friction that other tools introduce to the process. 

Join us for an InterSystems-hosted webinar to see how Deltanji provides an enterprise-grade source control solution that is tightly integrated with your IRIS environment.

A Continuous Training (CT) pipeline formalises a Machine Learning (ML) model developed through data science experimentation, using the data available at a given point in time. It prepares the model for deployment while enabling autonomous updates as new data becomes available, along with robust performance monitoring, logging, and model registry capabilities for auditing purposes.

InterSystems IRIS already provides nearly all the components required to support such a pipeline. However, one key element is missing: a standardised tool for model registry.

Introduction

FHIR (Fast Healthcare Interoperability Resources) is the modern standard for storing and exchanging clinical data. But once your data is in a FHIR server, how do you actually explore it? FHIR data is stored as JSON — powerful, but not practical to read directly. I wanted a tool where you could click on a patient, see their conditions, medications, lab results, and more — in a clean, readable format. So I built the FHIR Patient Viewer.

How It Works

The app runs entirely in Docker and connects directly to an InterSystems IRIS for Health FHIR server.

Working with files often starts off simple. open the file, read, and process. That approach works perfectly well, until the file happens to be an Excel file.

A Common Assumption

At first, an Excel file (.xlsx) looks like just another data file, rows, columns and values. nothing unusual. So it's natural to assume it can be read the same way as a .txt ot .csv file. But that's where things start to break.

Why Excel files behave differently

The key difference is how the data is stored:

-> .txt / .csv - plain text, line-by-line.

-> .

v2026.1 was just released as GA, and one of the features I'm looking forward to using is the DTL Explainer feature.

This allows you to take a Data Transformation, and with a click of a button get a human-readable description of the transformation (which you can also use as the basis for the DTL Description).

For complex DTLs, especially ones you didn't write yourself, or you did but a long time ago, this will allow you to get a clear quick understanding of what it's doing.