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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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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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.
If you're deploying to more than one environment/region/cloud/customer, you will inevitably encounter the issue of configuration management.
While all (or just several) of your deployments can share the same source code, some parts, such as configuration (settings, passwords) differ from deployment to deployment and must be managed somehow.
In this article, I will try to offer several tips on that topic. This article talks mainly about container deployments.
How suitable is Docker for standalone deployment of an Ensemble version and Ensemble application together?
The context is deployment by an application partner of an integration application and the supporting Ensemble version as a single package (single file ideally), to multiple environments and to multiple customer sites.
I don't have experience with Ensemble on Docker so I'm wondering what gaps and pitfalls may exist.
In this series of articles, I'd like to present and discuss several possible approaches toward software development with InterSystems technologies and GitLab. I will cover such topics as:
Git 101
Git flow (development process)
GitLab installation
GitLab Workflow
Continuous Delivery
GitLab installation and configuration
GitLab CI/CD
Why containers?
Containers infrastructure
CD using containers
CD using ICM
In this article, we'll build Continuous Delivery with InterSystems Cloud Manager. ICM is a cloud provisioning and deployment solution for applications based on InterSystems IRIS. It allows you to define the desired deployment configuration and ICM would provision it automatically. For more information take a look at First Look: ICM.
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.
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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.
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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”).
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I was wondering, what is your favourite public cloud provider?
What is your level of engagement with the cloud provider? Are you testing their infrastructure? In which case I'd expect you being progressing a parallel test with at least a second provider ;-) Or, are you already running a production environment?
--
OK, so, let me be the first one to share my experience with one specific cloud provider.
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This summer the Database Platforms department here at InterSystems tried out a new approach to our internship program. We hired 10 bright students from some of the top colleges in the US and gave them the autonomy to create their own projects which would show off some of the new features of the InterSystems IRIS Data Platform. The team consisting of Ruchi Asthana, Nathaniel Brennan, and Zhe “Lily” Wang used this opportunity to develop a smart review analysis engine, which they named Lumière. As they explain:
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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.
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Watch this video to learn how to use the GitUI plugin in the HealthShare Health Connect Cloud user interface to make changes to your interfaces in the Management Portal and commit them directly to your source control workflow.
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Watch this video to explore InterSystems TotalView For Asset Management, the fully managed cloud software that unifies data, automates data processing tasks, and empowers asset management firms with seamless data integration and flexibility.
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Hi all! We have just released a new online course, Getting Started with ICM, that provides an introduction to InterSystems Cloud Manager (ICM) -- one of the new technologies coming with the release of InterSystems IRIS!
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