I am happy to share with you my first experience of using a docker container version of IRIS for Health to explore your interest in using or having a trial by taking the advantage of a docker container that is lightweight, and easy to deploy. This cookbook will go through the implementation steps using the GitHub repository called ENSDEMO written by Renan Lourenco.

Some of you might not be aware of this "Known Issue/Limitation" when defining a BPL-based Business Process.

And those who are not might have encountered this (somewhat "unpleasant" error):

Creating information dashboards, pivot tables, and widgets is an important step in analysis that provides valuable sources of information for informed decision-making. The IRIS BI platform offers many opportunities to create and customize these elements. In this article, we will take a closer look at the basic techniques for developing them and the importance of using them.

So I know it's been a while, and I hate to let my adoring fans down... just not enough to actually start writing again. But the wait is over and I'm back! Now bask in my beautiful ginger words!

For this series, I am going to look at some common problems we see in the WRC and discuss some common solutions. Of course, even if you find a solution here, you are always welcome to call in and expression you gratitude, or just hear my voice!

This week's common problem: "My query returns no data."

If you have an app that uses some Caché client Windows components that are not included into CacheODBC distribution (e.g. CacheActiveX.dll), you need to proceed Caché client installation on end user's client computers and/or MS Terminal Servers. Being a part of Caché client's installation, Caché Cube is installed along with other components and is autostarted with every user's session. So, it becomes visible to every user.

To make it completely invisible, you can just move CACHE.lnk file from

By default, Analyzer executes queries as components are added. Often, there are times where you may know exactly what you are looking to do, and you do not want Analyzer to execute 5 queries as you drag and drop items onto rows/columns and select your filters. This is where toggling Auto-execute off can help.

There is an interesting new feature in the recently announced 2024.1 preview, JSON_TABLE. JSON_TABLE is one of a family of functions introduced by the 2016 version of the SQL Standard (ISO Standard, published in early 2017). It allows for JSON values to be mapped to columns and queried using SQL. JSON_TABLE is valid in the FROM clause of some SQL statements.

The syntax of JSON_TABLE is quite large, allowing for exceptional conditions where the provided JSON values don't match expectations, nested structures and so on.

Since we announced last week that Atelier would only be available as an Eclipse plugin, some of you have been asking about how to convert from your standalone client. Here's what you might want to do.

K9s is a terminal-based UI (aka kubectl clown suit), to manage Kubernetes clusters that drastically simplifies navigating, observing, and managing your applications in K8s, including Custom Resources like the InterSystems Kubernetes Operator (IKO) and ArgoCD Applications. If you are about to take your CKD, CKA, or CKS, leave k9s well enough alone for awhile as the abstraction to kubectl will become the standard for navigating the cluster and you will undoubtedly become estranged to the extended flags of kubectl and bomb the exam.

In the first part, I've shown how to start a new project on Django, as well as define new models and add already existing models.

This time, I'll introduce an admin panel, available out of the box and how it can be useful.

Important note: do not expect that if you try to repeat actions from this post it will work for you, it does not. During the article, I had to do some fixes in the django-iris project, and even in DB-API driver made by InterSystems to fix some issues there as well, and I think this driver is still in development, and we will get more stable driver in future. Let's decide that this article only explains how it could be if we would have all done.

A few months ago, I read this interesting article from MIT Technology Review, explaing how COVID-19 pandemic are issuing challenges to IT teams worldwide regarding their machine learning (ML) systems.

Such article inspire me to think about how to deal with performance issues after a ML model was deployed.

We're excited to continue to roll out new features to InterSystems IRIS Cloud SQL, such as the new Vector Search capability that was first released with InterSystems IRIS 2024.1. Cloud SQL is a cloud service that offers exactly that: SQL access in the cloud. That means you'll be using industry-standard driver technologies such as JDBC, ODBC, and DB-API to connect to this service and access your data. The documentation describes in proper detail how to configure the important driver-level settings, but doesn't cover specific third-party tools as - as you can imagine - there's an infinite number of them.

In this article, we'll complement that reference documentation with more detailed steps for a popular third-party data visualization tool that several of our customers use to access IRIS-based data: Microsoft Power BI.

A group of students at the Chalmers University of Technology (Gothenburg, Sweden) tried different approaches to automatically rating the quality of emergency calls, including iKnow.

Excerpt: "The most impressive results produced by iKnow is its ability to correctly classify 100% of the calls using the Average algorithm. This is quite surprising since iKnow only compares low-level concepts, how words relates to each other."

I heard about Message Bank when we started redesigning a Health Connect production to run in containers in the cloud. Since there will be multiple IRIS containers, we were directed to utilize Message Bank as one place to view messages and logs from all containers.