++Update: August 2, 2018

This article provides a reference architecture as a sample for providing robust performing and highly available applications based on InterSystems Technologies that are applicable to Caché, Ensemble, HealthShare, TrakCare, and associated embedded technologies such as DeepSee, iKnow, Zen and Zen Mojo.

Azure has two different deployment models for creating and working with resources: Azure Classic and Azure Resource Manager. The information detailed in this article is based on the Azure Resource Manager model (ARM).

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Database systems have very specific backup requirements that in enterprise deployments require forethought and planning. For database systems, the operational goal of a backup solution is to create a copy of the data in a state that is equivalent to when application is shut down gracefully.  Application consistent backups meet these requirements and Caché provides a set of APIs that facilitate the integration with external solutions to achieve this level of backup consistency.

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In this article, we’ll build a highly available IRIS configuration using Kubernetes Deployments with distributed persistent storage instead of the “traditional” IRIS mirror pair. This deployment would be able to tolerate infrastructure-related failures, such as node, storage and Availability Zone failures. The described approach greatly reduces the complexity of the deployment at the expense of slightly extended RTO.

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A lot of developers like to work with Studio and have been looking into source code version control such as GIT or into enabling modern development workflows like CICD or DevOps processes.

This article describe an elementary solution to get you started in CICD and DevOps, even if you are not yet ready to move to Atelier or forth coming VS Code approach which enable client side source code version control.

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Making a Chart using Intersystems IRIS + Python

How to use the IRIS Native API in Python to access globals and plot some charts.

Why Python?

With a large adoption and use in the world, Python have a great community and a lot of accelerators | libraries to deploy any kind of application.
If you are curious (https://www.python.org/about/apps/)

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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).

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We’ve already considered how to run an IRIS-based application in GCP Kubernetes in Deploying InterSystems IRIS Solution into GCP Kubernetes Cluster GKE Using CircleCI. Additionally, we’ve seen how to run an IRIS-based application in AWS Kubernetes in Deploying a Simple IRIS-Based Web Application Using Amazon EKS. Now, let’s look at how to deploy an application to the Azure Kubernetes Service (AKS).

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Hi,

I have wrote an article about how to install the intersystems cache driver in a Docker container, and then deploy it using Azure Functions: 

How to run a (Python) Azure Function as a Docker container & Deploy it using Bicep | Victor Sanner

This might be useful to others, especially the dockerfile which I have copied below. This builds a debian docker container and installs the Intersystems Cache driver, which python can then use :)

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