#Cloud

Hey Developers,

We're pleased to invite you to join the next InterSystems IRIS 2020.1 Tech Talk: DevOps on June 2nd at 10:00 AM EDT!

In this InterSystems IRIS 2020.1 Tech Talk, we focus on DevOps. We'll talk about InterSystems System Alerting and Monitoring, which offers unified cluster monitoring in a single pane for all your InterSystems IRIS instances. It is built on Prometheus and Grafana, two of the most respected open source offerings available.

Next, we'll dive into the InterSystems Kubernetes Operator, a special controller for Kubernetes that streamlines InterSystems IRIS deployments and management. It's the easiest way to deploy an InterSystems IRIS cluster on-prem or in the Cloud, and we'll show how you can configure mirroring, ECP, sharding and compute nodes, and automate it all.

Finally, we'll discuss how to speed test InterSystems IRIS using the open source Ingestion Speed Test. This tool is available on InterSystems Open Exchange for your own testing and benchmarking.

Hey Community,

Common tools for meeting the challenges of testing large-scale solutions deployed in the cloud:

⏯ Using Automated Testing to Build Reliable, Large-Scale API Server in the Cloud

Hey Community,

Exciting results from a massive workload to test InterSystems API. We'll describe how we're trying to push the boundaries of the current technology in the public cloud:

⏯ Running Large-Scale Rest API in the Cloud

Hey Developers,

New video is already on InterSystems Developers YouTube channel:

⏯ What is the InterSystems IRIS FHIR Server

Hi Community,

We'll look at moving InterSystems workload to the public cloud, then take a deep dive into the current architectures on the InterSystems cloud platform:

⏯ InterSystems Solutions in AWS

Hi All! For those of you who attended experience labs at the 2021 Virtual Summit, you may recall that one of the lab sessions was around Kubernetes. We've now converted that lab to be fully on-demand. You can launch a small cluster of VMs and follow the exercise to manage your Kubernetes cluster, deploy InterSystems IRIS containers to it, and watch its self-healing nature when destroying a pod.

It's a great introduction to Kubernetes if you are interested! See here: Achieving High Availability with InterSystems IRIS and Kubernetes

Want a commercial grade FHIR® Implementation included in your micro service ecosystem and barely have enough time to fill out your Health plan elections?

Hi Community,

New video is already on InterSystems Developers YouTube:

⏯ Fast & Efficient Provisioning & Testing with InterSystems Cloud Manager

Hi all,

I'm deploying IRIS for Health on a Google Cloud VM, and I note that the SuperServer port is 1971, rather than good old 1972. Is this as it's supposed to be? I can't see that it's documented anywhere.

Cheers,

Otto

I wanted to write it as a comment to article of @Evgeny Shvarov . But it happens to be so long, so, decided to post it separately.

I would like to add a bit of clarification about how docker uses disk space and how to clean it. I use macOS, so, everything below, is mostly for macOS, but docker commands suit any platform.

I have been working on redesigning a Health Connect production which runs on a mirrored instance of Healthshare 2019. We were told to take advantage of containers. We got to work on IRIS 2020.1 and split the database part from the Interoperability part. We had the IRIS mirror running on EC2 instances and used containers to run IRIS interoperability application. Eventually we decided to run the data tier in containers as well.

Being equipped by science and technology, human being have walked a long way by great inventions such as steam-engines or aeroplannes; while after decades, people gradually recognize that single creation could not lauch an industry-boom again. That is why and when, technologies grow up with a community, where are we now=P. An eco-system of technology would be born with the power of a system and grow up with the power of system-science, such as InterSystems, with which seated the letters "s-y-s-t-e-m". Graduated with M.S.

Hey Community,

Enjoy watching the new video on InterSystems Developers YouTube:

⏯ Creating an HL7 v2 to FHIR Transformation Pipeline with HealthLake Target

Challenges of real-time AI/ML computations

We will start from the examples that we faced as Data Science practice at InterSystems:

• A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
• An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
• A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?