InterSystems IRIS Document Database (DocDB) offers a flexible and dynamic approach to managing database data. DocDB embraces the power of JSON (JavaScript Object Notation), providing a schema-less environment for storing and retrieving data.
It is a powerful tool, enables developers to bypass a ton of boiler plate code in interaction with existing applications, serialization, pagination and integration. the seamless flow of DocDB with Interoperability Rest services and operations, gives a big leap in API production and management.
for full DocDB documentation Here. in the context of this article i will showcase a use case in which DocDB will make a perfect fit.
Couple days ago, a customer approached me with the wish to enhance their existing legacy application, that uses SOAP (Web)Services so it shares the same authorization with their new application API based on REST. As their new application uses OAuth2, the challenge was clear; how to pass access token with SOAP request to the server.
After spending some time on Google, it turned out, that one of possible ways of doing so was adding an extra header element to the SOAP envelope and then making sure the WebService implementation does what is needed to validate the access token.
This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data into an IRIS database instance via SQL syntax, for demo purpose.
In this article I will explain how to display management portal dashboard by using Python Flask web and Boostrap framework with the help of embedded python
One of the pain points for maintaining HL7 interfaces is the need to run a reliable regression test upon deployment to new environments and after upgrades. The %UnitTest class allows unit tests to be created and packaged alongside interface code. Test data can also be maintained within the unit test class, allowing for quick and easily repeatable smoke-testing and regression testing.
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?
Hi all. Yesterday I tried to connect Apache Spark, Apache Zeppelin, and InterSystems IRIS. During the process, I experienced troubles connecting it all together and I did not find a useful guide. So, I decided to write my own.
While starting the development with IRIS we have a distribution kit or in case of Docker we are pulling the docker image and then often we need to initialize it and setup the development environment. We might need to create databases, namespaces, turn on/off some services, create resources. We often need to import code and data into IRIS instance and run some custom code to init the solution.
And there plenty of templates on Open Exchange where we suggest how to init REST, Interoperability, Analytics, Fullstack and many other templates with ObjectScript. What if we want to use only Python to setup the development environment for Embedded Python project with IRIS?
So, the recent release of Embedded Python template is the pure python boilerplate that could be a starting point for developers that build python projects with no need to use and learn ObjectScript. This article expresses how this template could be used to initialize IRIS. Here we go!
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).
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.
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!
This is a quickstart guide to IRIS for Linux systems administrators who need to be able to support the IRIS DB as well as other normal infrastructure tasks.
IRIS is a DB system from Intersystems. An IRIS DB can hold code (in the form of a Class) or data (in the form of Globals). IRIS DB are Linux files called IRIS.DAT.
This post provides guidelines for configuration, system sizing and capacity planning when deploying Caché 2015 and later on a VMware ESXi 5.5 and later environment.
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.
The traditional use of an IRIS production is for an inbound adapter to receive input from an external source, send that input to an IRIS service, then have that service send that input through the production.
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.
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.
Some people are lucky enough to have a totally separate environment to run production in.
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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
GitLab CI/CD
CI/CD with containers
This first part deals with the cornerstone of modern software development - Git version control system and various Git flows.
In the previous article we saw how we could recover a resource stored in the database of our particular HIS, so today we will see how we can add new records in our HIS whose origin is an FHIR resource that we receive in our system.
Below we define the environment we will be creating via a Custom Resource Definition (CRD). It lets us define something outside the realm of what the Kubernetes standard knows (this is objects such as your pods, services, persistent volumes (and claims), configmaps, secrets, and lots more). We are building a new kind of object, an IrisCluster object.
InterSystems IRIS provides a complete application development environment for building sophisticated data- and analytics-intensive applications that connect data and application silos. It is designed to work with all of the common development technologies in an open, standards-based fashion and supports both server-side and client-side programming.
One useful feature of our REST framework is the ability for a dispatch class to identify request prefixes and forward them to another dispatch class. This approach of modularizing your URL map will improve code readability, enable you to easily maintain separate versions of an interface, and provide a means to protect API calls that only certain users will be allowed to access.
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
In the previous article, we covered Git basics, why a high-level understanding of Git concepts is important for modern software development, and how Git can be used to develop software. Still, our focus was on the implementation part of software development, but this part presents:
GitLab Workflow - a complete software life cycle process - from idea to user feedback
Continuous Delivery - software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time. It aims at building, testing, and releasing software faster and more frequently.
We often run into connectivity problems with HealthShare (HS) deployments in Microsoft Azure that have multiple HealthShare components (instances or namespaces) installed on the same VM, especially when needing to communicate to other HS components while using the Azure Load Balancer (ILB) to provide mirror VIP functionality. Details on how and why a load balancer is used with database mirroring can be found this community article.
If you are a customer of the new InterSystems IRIS® Cloud SQL and InterSystems IRIS® Cloud IntegratedML® cloud offerings and want access to the metrics of your deployments and send them to your own Observability platform, here is a quick and dirty way to get it done by sending the metrics to Google Cloud Platform Monitoring (formerly StackDriver).
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