#Cloud

Loading your IRIS Data to your Google Cloud Big Query Data Warehouse and keeping it current can be a hassle with bulky Commercial Third Party Off The Shelf ETL platforms, but made dead simple using the iris2bq utility.

Let's say IRIS is contributing to workload for a Hospital system, routing DICOM images, ingesting HL7 messages, posting FHIR resources, or pushing CCDA's to next provider in a transition of care. Natively, IRIS persists these objects in various stages of the pipeline via the nature of the business processes and anything you included along the way. Lets send that up to Google Big Query to augment and compliment the rest of our Data Warehouse data and ETL (Extract Transform Load) or ELT (Extract Load Transform) to our hearts desire.

A reference architecture diagram may be worth a thousand words, but 3 bullet points may work out a little bit better:

• It exports the data from IRIS into DataFrames
• It saves them into GCS as .avro to keep the schema along the data: this will avoid to specify/create the BigQuery table schema beforehands.
• It starts BigQuery jobs to import those .avro into the respective BigQuery tables you specify.

In an earlier article (hope, you’ve read it), we took a look at the CircleCI deployment system, which integrates perfectly with GitHub. Why then would we want to look any further? Well, GitHub has its own CI/CD platform called GitHub Actions, which is worth exploring. With GitHub Actions, you don’t need to rely on some external, albeit cool, service.

In this article we’re going to try using GitHub Actions to deploy the server part of InterSystems Package Manager, ZPM-registry, on Google Kubernetes Engine (GKE).

Hi,

Can a Cache Mirror be used in the cloud ? (ie stand up a Primary and Backup member instances in a High Availability Cache Mirroring configuration)

I'm investigating the validity of this configuration, because I was of the understanding that this may not possible due to these cloud servers not (typically) having fixed ip addresses, which interferes with the Virtual IP settings for the mirror set.

Is this correct, and if there are workarounds (like Load Balancing ?) can I have details on how this should be configured ?

I want to add ports 9100 and 9101 in addition to 52773. I read on docker container documentation that this is not possible on a already ran image. Currently it starts the google cloud IRIS health container automatically without me able to specify the additional ports. How can I add ports to Google cloud IRIS Health container?

Hi Community!

I'm pleased to announce that InterSystems IRIS is available on Microsoft Azure Marketplace!

→ InterSystems IRIS on Azure Marketplace

This is the second public cloud offering of InterSystems IRIS after recently announced Google Cloud Platform support.

Hello Developers!

Have you ever had to convert HL7v2 messages to FHIR (Fast Healthcare Interoperability Resources) and found the process complicated and confusing? InterSystems is rolling out a new cloud based SaaS offering called InterSystems FHIR Transformation Service, which makes the process easy. We are excited to announce an Early Access Preview Program for our new offering, and we would love to have you kick the tires and let us know what you think! All you need is a free AWS account, with an S3 bucket to drop in your HL7v2 messages, and another S3 bucket to get your FHIR output.

Hey Developers,

We're pleased to invite you to the "Best practices of in-platform AI/ML" webinar by InterSystems on April 28th at 11:00 EST/17:00 CET.

We are ridiculously good at mastering data. The data is clean, multi-sourced, related and we only publish it with resulting levels of decay that guarantee the data is current. We chose the HL7 Reference Information Model (RIM) to land the data, and enable exchange of the data through Fast Healthcare Interoperability Resources (FHIR®).

Hi Community!

We are glad to invite every developer who uses ObjectSript and VSCode plugin to the second webinar hold by the VSCode ObjectScript plugin developer on May 26 at 11:00 EDT.

You will learn how to develop InterSystems IRIS solutions using GitHub Development Flow with VSCode ObjectScript and Docker.

Speaker: @Dmitry Maslennikov, InterSystems Developers Advocate, CTO at CaretDev.

In this article, I’d like to tell you about a startup Nanteo which is one of the first batch of startups at InterSystems FHIR startup incubator — Caelestinus.

Who are we and what do we do?

Let assume you have a infinitely scaling algorithm implemented in your application, using replication, ECP, or any other means of horizontal scaling, and let assume you know how to run your system under any volume of requests, the trick is to deploy required number of computing nodes in the cluster. If we are talking about cluster of 2-4 nodes your administrator (or as they call it today "devops engineer") will install anything manually. Probably he will easily handle 5 nodes configuration in the cluster.

This article is a continuation of Deploying InterSystems IRIS solution on GKE Using GitHub Actions, in which, with the help of GitHub Actions pipeline, our zpm-registry was deployed in a Google Kubernetes cluster created by Terraform. In order not to repeat, we’ll take as a starting point that:

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)?