This concept may be known to some, but I just found it very useful and I would like to share as it may help someone else.
If you are working with CSP or Zen you sometimes come across the need to use embedded JavaScript. Suppose you are working with some loops, which use < or > as shown in example below:
Have you tried the InterSystems learning platform lab for IRIS IntegratedML? In that lab you can train and test a model on a readmission dataset and be able to predict when a patient will be readmitted or not, or calculate its probability of being readmitted.
You can try it without any installation on your system, all you have to do is start a virtual lab environment (Zeppelin) and play it around!
Deploying InterSystems HealthShare code, supporting lookups and artifacts like ssl certs, keys etc is relatively straight forward using Gitlab Runners. Not only does this approach enable managing the code base and deploying with git type workflows, but it also lends to a speedy recovery and repeatable environments for some implementations.
Want a commercial grade FHIR® Implementation included in your micro service ecosystem and barely have enough time to fill out your Health plan elections?
Testing ECP-based applications often take quite some effort for setup and preparation. I have created a Docker-based workbench that allows you to have it quick at hands. And if you crash it? You just give your containers a fresh start. The whole setup runs code-based during the start-up of your instance. In that sense, it is also a portable coding example using ZPM and the objectscript-docker-template
I have just created a new Global Master Topic, "IRIS Cheatsheets". IRIS has introduced a lot of new functionality, especially in scripting languages, FHIR R4 support, enhanced Interoperability Tools, and IRIS Analytics. Having spent 35 years working on Windows-based PC's and Laptops, I have surprisingly little knowledge of Linux, Docker and Git. Furthermore, I have written almost every application and Interface in ObjectScript with splatterings of SQL, .Net, and Java Gateways and the most basic knowledge of WinSCP, Putty, SSH. All that changed when I received my first Raspberry Pi.
Talking with a friend of mine, Machine Learning specialist @Renato Banzai, he brought one of the biggest challenges faced by companies nowadays: deploying ML/AI in live environments.
In the previous article, [What is a Production?], we checked the production contents. We ran the sample code and checked the flowing messages' contents into the production on the Visual Trace page.
For those of you who might be new to IRIS, and even those who have used Cache or IRIS for some time but want to explore beyond its usually-assumed boundaries and practices, you might want to dive into this detailed exploration of the database engine that is at its heart, and discover just what you can really do with it, going way beyond what InterSystems have done with it for you.
During the last weeks, I was working on various issues and problems related to SW development. I found that quite often problem analysis was mostly chasing issues just on the surface but not really attacking the deeper reasons of the problem and follow the consequences. It's like the doc that stops your leg bleeding but doesn't see that it is broken.
In the first installment of this article series, we discussed how to read a “big” chunk of data from the raw body of an HTTP POST method and save it to a database as a stream property of a class. Now let’s look at how to save such data and metadata in JSON format.
This is the third article in our short series around innovations in IRIS SQL that deliver a more adaptive, high-performance experience for analysts and applications querying relational data on IRIS. It may be the last article in this series for 2021.2, but we have several more enhancements lined up in this area. In this article, we'll dig a little deeper into additional table statistics we're starting to gather in this release: Histograms
Millions of professionals use a wonderful tool, spreadsheets, for engineering calculations and financial analysis. It attracts a user-friendly interface and clear data organization. Cell formulas provide rich opportunities for automating calculations. No programming is required.
Apache Zeppelin it's a Multi-purpose notebook that allow you:
Data Ingestion
Data Discovery
Data Analytics
Data Visualization and Collaboration.
Apache Zeppelin interpreter concept allows any language/data-processing-backend to be plugged into Zeppelin. Currently Apache Zeppelin supports many interpreters such as Apache Spark, Apache Flink, Python, R, JDBC, Markdown and Shell.
In this post, we would like to tell you how to take the most out of the Developer Community, to learn as much as you can from the InterSystems experts on the technology!
Pay attention to these steps to become an advanced user of our community!
For a long time, we have been using a utility in production to export the result of a query to an Excel spreadsheet. Moreover, we have applied a modification of it, in which the explicit setting of column formats is a priority.
It's a challenge when you need, as a software architect, design a corporate architecture to meet the current business requirements, you need achieve level 5. With InterSystems IRIS. it's possible. With 1 product you get SQL + NoSQL + ESB + BI + Open Analytics + Real time virtual cubes + NLP + AutoML + ML (with Python) and Advanced cloud + Sharding support.
Organizations around the world lose an estimated five percent of their annual revenues to fraud, according to a survey of Certified Fraud Examiners (CFEs) who investigated cases between January 2010 and December 2011. Applied to the estimated 2011 Gross World Product, this figure translates to a potential total fraud loss of more than $3.5 trillion (source: https://www.acfe.com/press-release.aspx?id=4294973129).
The InterSystems IRIS has a good connector to do Hadoop using Spark. But the market offers other excellent alternative to Big Data Hadoop access, the Apache Hive. See the differences:
I'd like to share with you some storage features that also exist in Caché but are almost unknown and mostly not used. They are of course available in IRIS and gain importance with large and distributed storage architectures.
When you have been using cubes for business intelligence in a namespace for some time, you may find that there are many cubes in the namespace, only some of which are actively being used. However, it can be difficult to tell which cubes users are or are not querying, and maintaining unused cubes can be costly both in terms of storage and of computation to keep them up to date. This article provides some suggestions and examples for monitoring which cubes are in active use, and for removing cubes that you determine are no longer necessary.