With Embedded Python and the Native API, it is becoming increasingly natural to write part of IRIS application logic in Python. But one question quickly comes up: how can you manipulate IRIS persistent objects from Python without losing the connection to the native object model, class dictionary, indexes, storage, and SQL projections?

iris-persistence explores that question. The project provides a Python object persistence layer for InterSystems IRIS, inspired by %Persistent

I am trying to connect to hive database and IRIS Intersystems Databases using jaydebeapi in python. I am able to connect to one database at a time. While trying to connect to other database, I am getting the below error

"Class org.apache.hive.jdbc.HiveDriver is not found"

or 

"Class com.intersystems.jdbc.IRISDriver is not found"

lin1 -

hive_con = jd.connect(java_driver_class, jdbc_conn_url, [hive_user, hive_pass],jarfile)

lin2 -

iris_con = jd.connect(iris_driver_class, iris_conn_url, [iris_user, iris_pass],jarfile)

Hello everyone.

After trying to run Python methods in the intersystemsdc/irishealth-community Docker container, which no longer allows any Python methods to be executed, I decided to abandon this version and start working with containers.intersystems.com/intersystems/iris-community.

In this environment, the Python methods work, which was a significant improvement.

I'm trying to import my libraries with pip install -r requirements.txt

InterSystems IRIS globals are one of the platform's core strengths: they store hierarchical data in a direct, ordered, and efficient structure. But when working from Python, manipulating globals can sometimes feel closer to a low-level API than to the natural habits of the language.

The iris-global-reference project provides a Python layer on top of IRIS globals. Its goal is simple: make access to globals more readable, more idiomatic, and easier to integrate into modern Python code, without hiding the underlying hierarchical model.

When developing Python applications with InterSystems IRIS, you can quickly end up with several execution contexts:

• Python launched directly by IRIS with Embedded Python;
• a regular python3 process that loads the Embedded Python libraries from a local IRIS installation;
• an external Python application that connects to IRIS through the official native driver.

These three cases are useful, but they do not behave exactly the same way for imports, system configuration, object APIs, and SQL access.

This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI.

This article is based on this Kaggle Notebook, which you can run and directly edit yourself.

Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks.

This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process.

Abstract

Solar irradiance forecasting is critical for grid stability in photovoltaic (PV) power plants. This article replicates and extends the methodology of Lara-Benítez et al. (2023) "Short-term solar irradiance forecasting in streaming with deep learning" replacing the original offline simulation with a fully operational streaming pipeline built on InterSystems IRIS. We leverage IRIS Interoperability Productions as the streaming backbone, Embedded Python to run MLP, LSTM, and CNN deep learning models, and IntegratedML as an AutoML baseline.

This article introduces SHAP explainability methods as an approach to understand the reasons behind predictions in machine learning black-box models. It also includes a simple Jupyter notebook that you can use and modify to gain hands-on experience with these concepts:

https://www.kaggle.com/code/jorgeivnjh/explainability-in-ml-models

https://github.com/JorgeIvanJH/Explainability-in-ML-models

We will leverage these concepts for a future implementation in our Continuous Training Pipeline: https://community.intersystems.com/post/complementing-iris-mlflow-continuous-training-ct-pipeline

Today I have published a new Open Exchange package for generation of Synthetic Data directly into IRIS.

 It can be a frustrating process to find decent datasets when you are looking to make a demo app. Maybe the dataset doesn't matter that much, but you still want it to appear somewhat genuine and with several linked tables that are usable directly within IRIS with the neat implicit joins with ->. Maybe you just want linked tables that are easily installable with IPM to benchmark queries, this dataset generation would be perfect.

What is a Microservice?

A microservice is an architectural style that structures an application as a collection of small, autonomous services. Each component is developed around a specific business capability, can be deployed independently, and is typically managed by a miniature, specialized, self-governing team. (Source: https://microservices.io/)

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.

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.

-> .

Hi all.

I have a rather strange problem.

I've created a method in Python to create a vector for a vector search. So far, so good.

If I call this method from the terminal, it works correctly:

But if I make this same call from a code block in a Business Process, it gets stuck, doesn't respond, and throws the following error:

Does anyone know what's happening and how to fix it?

Thank you in advance

Why This Integration Matters

InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.

But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.

Global Guard AI

1 Introduction

In environments that use InterSystems IRIS, globals are the physical foundation of data storage. Although system queries and administrative tools exist for metric inspection, global growth analysis is usually reactive: the problem is generally only noticed when there is disk pressure or performance impact.