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.
If you do not have direct access to the server that runs your IRIS Docker container you still may require access to the container outside "iris session" or "WebTerminal". With an SSH terminal (PuTTY, KiTTY,.. ) you get access inside Docker, and then, depending on your needs you run "iris session iris" or display/manipulate files directly.
Note:
This is not meant to be the default access for the average application user but the emergency backdoor for System Management, Support, and Development.
Like hardware hosts, virtual hosts in public and private clouds can develop resource bottlenecks as workloads increase. If you are using and managing InterSystems IRIS instances deployed in public or private clouds, you may have encountered a situation in which addressing performance or other issues requires increasing the capacity of an instance's host (that is, vertically scaling).
InterSystems Learning Services is working to identify and create libraries of high-quality learning resources for third-party technologies, platforms, and systems that are part of, integrated with, or commonly used with InterSystems products and technologies. we don't create content for these ourselves, but want to support our clients, external and internal, in learning about them and how to use them.
What is Distributed Artificial Intelligence (DAI)?
Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).
Anton Umnikov Sr. Cloud Solutions Architect at InterSystems AWS CSAA, GCP CACE
AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores.
Most CloudFormation articles are Linux-based (no wonder), but there seems to be a demand for automation for Windows as well. Based on this original article by Anton, I implemented an example of deploying a mirror cluster to Windows servers using CloudFormation.I also added a simple walk through.
The complete source code can be found here.
Update: 2021 March 1 I added a way to connect to Windows shell by public key authentication via a bastion host as a one-liner.
Episode 16 of Data Points features a conversation with Jeff Fried — Director of Product Management for Data Platforms at InterSystems — about what it means for InterSystems to have been named a visionary in Gartner's first-ever magic quadrant for Cloud Database Management Systems.
I would like to offer my services as an Intersystems Professional and am available to work on projects.
I have more than a decade experience into Intersystems stack of technologies including IRIS, Ensemble, Healthshare, Healthconnect, Cache Objectscript, Mumps, Zen, Analytics etc. with companies spread over US and UK involved in multiple domains.
Does anyone has any idea on how to send an encrypted / secure Print from IRIS ( which is hosted on AWS ) to a printer ( which is an on-premise device ).
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.
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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