Cloud computing is an information technology paradigm that enables ubiquitous access to shared pools of configurable system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the Internet.
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ML Toolkit for InterSystems IRIS, besides Python/R/Julia, allows orchestrating cloud-based advanced analytics services, such as Microsoft Azure Data Factory and Machine Learning:
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:
With this article, I would like to show you how easily and dynamically System Alerting and Monitoring(or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation).
Imagine you want to see what InterSystems can give you in terms of data analytics. You studied the theory and now you want some practice. Fortunately, InterSystems provides a project that contains some good examples: Samples BI. Start with the README file, skipping anything associated with Docker, and go straight to the step-by-step installation. Launch a virtual instance, install IRIS there, follow the instructions for installing Samples BI, and then impress the boss with beautiful charts and tables. So far so good.
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Hi, someone have connected Iris to a Bucket ? like aws, huawei, etc.
I try to use private Bucket as storage for files, and Iris as database & API interface to manage this files, but al SDKs are in another languages, no one have founded on COS.
AWS has officially released their second-generation Arm-based Graviton2 processors and associated Amazon EC2 M6g instance type, which boasts up to 40% better price performance over current generation Intel Xeon based M5 instances.
A few months ago, InterSystems participated in the M6g preview program, and we ran a few benchmarks with InterSystems IRIS that showed compelling results. This led us to support ARM64 architectures for the first time.
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).
In this InterSystems IRIS2020.1 Tech Talk, we’ll focus on using InterSystems Managed FHIR Service in the AWS Cloud. We’ll start with an overview of FHIR, which stands for Fast Healthcare Interoperability Resources, and is a next generation standards framework for working with healthcare data.
You'll learn how to:
provision the InterSystems IRIS FHIR server in the cloud;
integrate your own data with the FHIR server;
use SMART on FHIR applications and enterprise identity, such as Active Directory, with the FHIR server.
We will discuss an API-first development approach using the InterSystems IRIS FHIR server. Plus, we’ll cover the scalability, availability, security, regulatory, and compliance requirements that using InterSystems FHIR as a managed service in the AWS Cloud can help you address.
Last time we deployed a simple IRIS application to the Google Cloud. Now we’re going to deploy the same project to Amazon Web Services using its Elastic Kubernetes Service (EKS).
We assume you’ve already forked the IRIS project to your own private repository. It’s called <username>/my-objectscript-rest-docker-template in this article. <root_repo_dir> is its root directory.
Before getting started, install the AWS command-line interface and, for Kubernetes cluster creation, eksctl, a simple CLI utility. For AWS you can try to use aws2, but you’ll need to set aws2 usage in kube config file as described here.
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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.
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Most of us are more or less familiar with Docker. Those who use it like it for the way it lets us easily deploy almost any application, play with it, break something and then restore the application with a simple restart of the Docker container.
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I've been working on deploying an IRIS for Health environment in EKS. There is a video session in the InterSystems learning portal about this feature but I have not succeeded in finding the proper documentation and resources to use this in my Kubernetes cluster.
Has this been deprecated/discontinued? Any idea where can I find the resources? Should I stick to StatefulSets instead of using the IrisCluster resource type provided by this operator?
Last time we launched an IRIS application in the Google Cloud using its GKE service.
And, although creating a cluster manually (or through gcloud) is easy, the modern Infrastructure-as-Code (IaC) approach advises that the description of the Kubernetes cluster should be stored in the repository as code as well. How to write this code is determined by the tool that’s used for IaC.
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.
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