InterSystems IRIS is a Complete Data Platform InterSystems IRIS gives you everything you need to capture, share, understand, and act upon your organization’s most valuable asset – your data. As a complete platform, InterSystems IRIS eliminates the need to integrate multiple development technologies. Applications require less code, fewer system resources, and less maintenance.
Say I've been developing a web application that uses IRIS as the back end. I've been working on it with unauthenticated access. It's getting to the point where I would like to deploy it to users, but first I need to add authentication. Rather than using the default IRIS password authentication, I'd like users to sign in with my organization's Single Sign On, or some other popular identity provider like Google or GitHub. I've read that OpenID Connect is a common authentication standard, and it's supported by IRIS. What is the simplest way to get up and running?
This is the sixth in a series of releases that are part of the developer preview program for 2022.2 Future preview releases are expected to be updated biweekly and we will add features as they are ready. Many updates, fixes and enhancements have been added in 2022.2, in SQL management, cloud integration, Kafka and JMS adapters, the SQL Loader, and other areas.
I am recruiting on a fully remote Intersystems Developer. This role will be a long term contract to begin with high likelihood of extensions or conversion permanent. Please check out the job description down below and feel free to send me an email with your resume: Spencer.Frey@insightglobal.com
Over time I have created an house-automation solution based on IRIS: 90% of my code is pure ObjectScript, with the most recent 10% being the use of Python libraries for specific tasks. All of the above being terminal based up to now.
I would like to expose some configuration options / parameters via a very simple web page, to be serviced with the IRIS private web service (so I don't want to use an external Webserver just for this..)
Note sure if anyone would know this.... But I presented my team with a Proof of Concept of running SAM to monitor our IRIS Development and Test Clusters. In talking with them we would like additional OS metrics that aren't provided by what is built into SAM. Looking at more OS detail I found node_exporter from Promethus. I added node_exporter to our server that we want to monitor, but then tried to config isc_prometheus.yml to use node_exporter. That did not go well and when I restarted SAM, it would not download the built in metrics to SAM.
Credentials for a Productions are stored as plain text in ^Ens.SecondaryData.Password and exposed as plain text via SQL table Ens_Config.Credentials which is not ideal as only admins should know the credentials.
I can create my own adapter etc... to store and use encrypted passwords but does anyone know if there is a standard way to do this in a Production?
Alternatively, am I missing how to secure this so the production can run and someone can monitor and operate a production without access to the SQL table or global?
In this webinar, we'll show you some of the general principles and problems of solving sustainability challenges, as well as some great ideas that came up in hackathons for inspiration and do's and don'ts.
Also, we’ll discuss and answer the questions on how to build interoperability solutions using InterSystems IRIS and IRIS for Health.
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.
This is the fifth in a series of releases that are part of the developer preview program for 2022.2 Future preview releases are expected to be updated biweekly and we will add features as they are ready. Many updates, fixes and enhancements have been added in 2022.2, in SQL management, cloud integration, Kafka and JMS adapters, the SQL Loader, and other areas.
In this GitHub we fine tune a bert model from HuggingFace on review data like Yelp reviews.
The objective of this GitHub is to simulate a simple use case of Machine Learning in IRIS : We have an IRIS Operation that, on command, can fetch data from the IRIS DataBase to train an existing model in local, then if the new model is better, the user can override the old one with the new one. That way, every x days, if the DataBase has been extended by the users for example, you can train the model on the new data or on all the data and choose to keep or let go this new model.