Hello Community! 👋
Welcome to the second part of the IRIS IO Utility series. This extension represents my submission for the InterSystems "Bringing Ideas to Reality" Contest 2025 and offers you an intuitive and powerful interface for importing and exporting data directly inside VS Code.

Apache Airflow is the leading open-source platform to programmatically author, schedule, and monitor data pipelines and workflows using Python. Workflows are defined as code (DAGs), making them version-controlled, testable, and reusable. With a rich UI, 100+ built-in operators, dynamic task generation, and native support for cloud providers, Airflow powers ETL/ELT, ML pipelines, and batch jobs at companies like Airbnb, Netflix, and Spotify.

Airflow Application Layout

Hello Developers! 👋
I’m excited to introduce IRIS IO Utility, my submission for the InterSystems "Bringing Ideas to Reality" Contest 2025. This VS Code extension provides you an intuitive and powerful interface for importing and exporting data without leaving your IDE.

InterSystems IRIS provides extensive configurable security options, yet many developers primarily use roles and resources to secure entire tables or routines. Today, we will delve deeper. We can also secure individual columns and rows separately, but these two mechanisms operate very differently. Let's begin with the columns.

Column Security

For testing and demonstration, we will keep our table structure concise and straightforward. We have a table called "Person" in the USER namespace that contains an ID column, a date of birth column (DOB), first name, and last name.

We're excited to announce the Early Access Program for IntegratedML Custom Models, a powerful new capability coming in IRIS 2026.1!

The Load Data utility it is an excellent tool to load data from CSV/TXT files into an IRIS SQL Table, but it is required send the target file to the IRIS server and write the Load Data sentence to ingest the file content. Now it is possible select a file in VSCode, set the table destination and submit the request. The vscode-load-data utility will send the file to the IRIS server and run the Load Data command to you! Very simple:

When using standard SQL or the object layer in InterSystems IRIS, metadata consistency is usually maintained through built-in validation and type enforcement. However, legacy systems that bypass these layers—directly accessing globals—can introduce subtle and serious inconsistencies.

Parallel query hinting boosts certain query performances on multi-processor systems via parallel processing. The SQL optimizer determines when this is beneficial. On single-processor systems, this hint has no effect.

Parallel processing can be managed by:

1. Setting the auto parallel option system-wide.
2. Using the %PARALLEL keyword in the FROM clause of specific queries.

%PARALLEL is ignored when it applied to:

Hi,

When we open a terminal in IRIS, we are entering the ObjectScript shell. Within this shell, we can execute IRIS commands, such as:

In today’s healthcare data landscape, FHIR has become the standard for structured clinical data exchange. However, while FHIR excels at interoperability, its JSON format makes analytics challenging—including FHIR QuestionnaireResponse.

Hey Community!

We're happy to share a new video from our InterSystems Developers YouTube:

⏯ Foreign Tables In 2025.2 @ Ready 2025

https://www.youtube.com/embed/pIMa7lqWiHc
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Hi Community,

In this article, we will explore the concepts of Dynamic SQL and Embedded SQL within the context of InterSystems IRIS, provide practical examples, and examine their differences to help you understand how to leverage them in your applications.

InterSystems SQL provides a full set of standard relational features, including the ability to define table schema, execute queries, and define and execute stored procedures. You can execute InterSystems SQL interactively from the Management Portal or programmatically using a SQL shell interface. Embedded SQL enables you to embed SQL statements in your ObjectScript code, while Dynamic SQL enables you to execute dynamic SQL statements from ObjectScript at runtime. While static SQL queries offer predictable performance, dynamic and embedded SQL offer flexibility and integration, respectively.

Window functions in InterSystems IRIS let you perform powerful analytics — like running totals, rankings, and moving averages — directly in SQL.
They operate over a "window" of rows related to the current row, without collapsing results like GROUP BY.
This means you can write cleaner, faster, and more maintainable queries — no loops, no joins, no temp tables.

In this article let's understand the mechanics of window functions by addressing some common data analisys tasks.

High-Performance Message Searching in Health Connect

The Problem

Have you ever tried to do a search in Message Viewer on a busy interface and had the query time out? This can become quite a problem as the amount of data increases. For context, the instance of Health Connect I am working with does roughly 155 million Message Headers per day with 21 day message retention. To try and help with search performance, we extended the built-in SearchTable with commonly used fields in hopes that indexing these fields would result in faster query times. Despite this, we still couldn't get some of these queries to finish at all.