This is a rather personal view of the history before Caché.
It is in no sense meant to compete with the excellent books from Mike Kadow discussed in an earlier article.
We have different histories and so this is meant to create a different perspective of the past.

Hello everyone

For those interested, here is an implementation example I used in one of my projects to interoperate with MongoDB using the pymongo package.

iris-mongodb

Interoperates with MongoDB (via pymongo package) with InterSystems IRIS (including outbound adapter)

Remember to import pymongo package.

Hi community,

The aim of this article is to explain how to create messaging between IRIS and Microsoft Teams.

In my company, we wanted to monitor error messages, and we used the Ens.Alert class to redirect those error messages through a Business Operation that sent an email.
The problem was that we sent those error messages to a support account where there were many emails. We wanted something specific for a specific team.

So we investigated how to make these messages reach the development team directly and they could have, in real time, a notification of an error in our production.
In our company we use Microsoft Teams as a corporate tool, so we asked ourselves: How could we make these messages reach the IRIS development team?

We all know that having a set of proper test data before deploying an application to production is crucial for ensuring its reliability and performance. It allows to simulate real-world scenarios and identify potential issues or bugs before they impact end-users. Moreover, testing with representative data sets allows to optimize performance, identify bottlenecks, and fine-tune algorithms or processes as needed. Ultimately, having a comprehensive set of test data helps to deliver a higher quality product, reducing the likelihood of post-production issues and enhancing the overall user experience.

In this article, let's look at how one can use generative AI, namely Gemini by Google, to generate (hopefully) meaningful data for the properties of multiple objects. To do this, I will use the RESTful service to generate data in a JSON format and then use the received data to create objects.

Hi Community,

The InterSystems Learning Website has many important iterative courses. So if you want to learn about InterSystems and start to work with InterSystems this is the path:

Overview

With the help of SQL, you can build, train, and manage machine learning models directly in the database with InterSystems IRIS's robust IntegratedML tool. Using SQL examples that represent your data, we will go over IntegratedML configuration and how it is used in practical situations in this article.

I want to address the nasty problems about reading a flat text in ASCII, UTF*
explicitly excluding HTML, EBCDIC, and other encoding.
According to Wikipedia there are at least 8 variations of control characters.

• CR+LF is typical for Windows
• LF is typical for the Linux/UNIX world
• CR is Mac's favorite

As you can deduct from the names the inspiration comes from mechanical typewriters.

First of all, what is data anonymization?

According to Wikipedia:

In the world of APIs, REST is very extended. But what happens when you need more flexibility in your data-fetching strategies? For instance letting the client to choose what fields is going to receive. Enter GraphQL, a query language for your APIs that provides a flexible alternative to REST.

In this post, we will:

• Compare REST and GraphQL.
• Dive into the basics of GraphQL: Queries, Mutations, and HTTP.
• Build a simple GraphQL server implementation using Graphene, SQLAlchemy, and Flask over data in InterSystems IRIS.
• Explore how to deploy your GraphQL server as a WSGI application in IRIS.

Recently I was fielding some questions that someone had about some bugs that crop up on Docker for Mac, and it reminded me of what Shakespeare wrote in his famous tragedy about large-scale software orchestration, Kubelet: the Prince of Benchmark.

In this article, we’ll build a highly available IRIS configuration using Kubernetes Deployments with distributed persistent storage instead of the “traditional” IRIS mirror pair. This deployment would be able to tolerate infrastructure-related failures, such as node, storage and Availability Zone failures. The described approach greatly reduces the complexity of the deployment at the expense of slightly extended RTO.

Hello!

This article is a small overview of a tool that helps to understand classes and their structure inside the InterSystems products: from IRIS to Caché, Ensemble, HealthShare.

In short, it visualizes a class or an entire package, shows the relations between classes and provides all the possible information to developers and team leads without making them go to Studio and examine the code there.

If you are learning InterSystems products, reviewing projects a lot or just interested in something new in InterSystems Technology solutions — you are more than welcome to read the overview of ObjectScript Class Explorer!