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.
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.
We return with our example of using the FHIR Adapter, in this article we are going to review how we can configure it in our IRIS instances and what the result of the installation is.
The steps taken to configure the project are the same as indicated in the official documentation, you can review them directly here. Well, let's get to work!
If you've worked with iKnow domain definitions, you know they allow you to easily define multiple data locations iKnow needs to fetch its data from when building a domain. If you've worked with DeepSee cube definitions, you'll know how they tie your cube to a source table and allow you to not just build your cube, but also synchronize it, only updating the facts that actually changed since the last time you built or synced the cube. As iKnow also supports loading from non-table data sources like files, globals and RSS feeds, the same tight synchronization link doesn't come out of the box. In this article, we'll explore two approaches for modelling DeepSee-like synchronization from table data locations using callbacks and other features of the iKnow domain definition infrastructure.
If you’ve ever wondered whether there is a way to regulate access to resources in Caché, wonder no more. In version 2014.2 special classes were added that allow developers to work with semaphores.
I am planning to implement Business Intelligence based on the data in my instances. What is the best way to set up my databases and environment to use DeepSee?
Managed File Transfer (MFT) feature of InterSystems IRIS enables easy inclusion of a third-party file transfer service directly into an InterSystems IRIS production. Currently, DropBox, Box, and Kiteworks cloud disks are available.
In this article, I'd like to describe how to add more cloud storage platforms.
In previous articles on iKnow, we described a number of demo applications (iKnow demo apps parts 1, 2, 3, 4 & 5) that are either part of the regular kit or can be easily installed from GitHub. All of those applications assumed you already had your iKnow domain ready, with your data of interest loaded and ready for exploration. In this article, we'll shed more light on how exactly you can get to that stage: how you define and then build a domain.
InterSystems IRIS Business Intelligence allows you to keep your cubes up to date in multiple ways. This article will cover building vs synchronizing. There are also ways to manually keep cubes up to date, but these are very special cases and almost always cubes are kept current by building or synchronizing.
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.
I have a few cubes and numerous dashboards and I am ready to deploy them to our end users and administrators. How to configure DeepSee so that users don’t disrupt each other’s areas and are restricted from using functionalities specific to developers?
In a conference call earlier this week, a customer described how they built an iKnow domain with clinical notes and now wanted to filter the contents of that domain based on the patient's diagnosis codes.
I needed to know programmatically if last ran failed or not.
After some exploring, here's the code:
ClassMethod isLastTestOk() As %Boolean
{
set in = ##class(%UnitTest.Result.TestInstance).%OpenId(^UnitTest.Result)
for i=1:1:in.TestSuites.Count() {
#dim suite As %UnitTest.Result.TestSuite
set suite = in.TestSuites.GetAt(i)
return:suite.Status=0 $$$NO
}
quit $$$YES
}
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:
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