Hi Dev Community

I thought i would share a little method I knocked together to traverse and compare 2 JSON objects for basic equivilance. I'm currently working on some data migration, and wanted a basic sanity check to validate that the JSON output is basically equivliant between the old and new, excluding a few things like timestamps.

It's a basic little recurvsive method, that will bubble up any differences over a nested structure. It's very low tech, as that's all I need it to do, but I thought it might be useful for others?

A concurrent session in IRIS:
SQL, Objects, REST, and GraphQL

Kazimir Malevich, "Athletes" (1932)

"But of course you don't understand! How can a person who has always traveled in a horse-drawn carriage understand the feelings and impressions of the express traveler or the pilot in the air?"

Kazimir Malevich (1916)

Introduction

We’ve already addressed the topic of why object/type representation is superior to SQL for implementing subject area models. And those conclusions and facts are as true now as they have ever been. So why should we take a step back and discuss technologies that drag abstractions back to the global level, where they had been in the pre-object and pre-type era? Why should we encourage the use of spaghetti code, which results in bugs that are hard to track down, and which relies only on virtuoso developer skills?

There are several arguments in favor of transmitting data via SQL/REST/GraphQL-based APIs as opposed to representing them as types/objects:

Hi developers!

Recently we announced the preview of Embedded Python technology in InterSystems IRIS.

Check the Sneak Peak video by @Bob Kuszewski.

Embedded python gives the option to load and run python code in the InterSystems IRIS server. You can either use library modules from Python pip, like numpy, pandas, etc, or you can write your own python modules in the form of standalone py files.

So once you are happy with the development phase of the IRIS Embedded Python solution there is another very important question of how the solution could be deployed.

One of the options you can consider is using the ZPM Package manager which is described in this article.

This is a demo to make use of a simple WebSocket Client with Embedded Python in IRIS.

How to Test it

• Run an Iris Session in Docker
• Select your WebSocket Echo Server
• Enter the text you want to send or generate it
• Send it and see the result

$ docker-compose exec iris iris session iris "##class(rccpy.WSockPy).Run()"

*** Welcome to WebSocket Embedded Python Demo ***

We use the Caché JDBC Gateway to Oracle and SQL servers to directly invoke their stored procedures from Ensemble. Getting quick, inline data results back are typically handled within the Functions.Library class as a function to wrap the query and format the return appropriately.

Image search like Google's is a nice feature that wonder me - as almost anything related to image processing.

A few months ago, InterSystems released a preview for Python Embedded. As Python has a lot of libs for deal with image processing, I decided to start my own attemptive to play with a sort of image search - a much more modest version in deed :-)

Making a blog using Python + IRIS Globals

Since I started to use internet (late 90's), I always had a CMS (content management system) present to make easier post
any information in a blog, social media or even an enterprise page. And later years putting all my code into github I
used to document it on a markdown file. Observing how easy could be persisting data into Intersystems IRIS with the
Native API I decided to make this application and force myself to forget a little of SQL and stay open to key-value database
model.

With https://openexchange.intersystems.com/package/IRIS-Publisher, you can document your IRIS Application and gererate the Application Documentation Web Portal (responsive). See the results:

Main Page:

Classes Menu and Class Page: The class page with class documentation, the list of methods and properties.

The pandemic that struck the world in 2020 made everyone follow the news and the numbers that involve the COVID-19.

Why don’t you take that opportunity to create something simple and pleasant, to follow the number of vaccinations worldwide?

To face this challenge, I'm using the data provided by Our World in Data - Research and data to make progress against the world’s largest problems.

They have a dedicated repository on Github with the data of COVID-19, and I took the vaccination data to help me with my tracker.

Artificial intelligence has solved countless human challenges – and medical coding might be next.
As organizations prepare for ICD-11, medical coding is about to become more complicated. Healthcare organizations in the United States already manage 140,000+ codes in ICD-10. With ICD-11, that number will rise.
Some propose artificial intelligence as a solution. AI could aid computer-based medical coding systems, identifying errors, enhancing patient care, and optimizing revenue cycles, among other benefits.

Built in multi model integration using InterSystems iris data platform

Solution of hospital information inquiry business

Integration of hospital information query business solutions using InterSystems IRIS data platform with built-in multiple models

### Summary:

Hello, developers!

I would like to share the history of the project - the ZAPM shell.

As soon as ZPM was implemented, I immediately began to think about how best to use it.

And immediately faced with the desire to move more quickly between namespaces, especially when there are more than 20 of them.
I had to leave the ZPM, move to the desired namespace and re-enter the shell.
I suggested an improvement - a new "namespace" command for easier navigation.
I didn’t wait - I did it myself. And so it went. If you need it, get ready to do it yourself.

Hi developers!

Often we need to deploy some data along with code pieces of the application.

And for InterSystems IRIS developers the question could sound: "How can I deploy the data I have in globals?"

Here I want to suggest to you one of the approaches - deploying global data using the ZPM package manager.

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.