This topic unites posts which describe approaches, tools, and solutions to import and export data into InterSystems IRIS and other InterSystems Data platform products: CSV, JSON, SQL, flat files, binary files, globals, streams, etc.
FHIR repositories, applications and servers typically serve clinical data in small quantities, whether to return data about a patient, their medications, vaccines, allergies, among other information. However, it is common for a large amount of data in FHIR/JSON format to be requested to be used to load into Data Lakes, identifying study cohorts, population health, or transferring data from one EHR to another. To meet these business scenarios that require large extractions and loads of data, it is recommended to use the FHIR Bulk Data Access feature provided by HL7 institution.
Dynamic Entities (objects and arrays) in IRIS are incredibly useful in situations where you are having to transform JSON data into an Object Model for storage to the database, such as in REST API endpoints hosted within IRIS. This is because these dynamic objects and arrays can easily serve as a point of conversion from one data structure to the other.
Dynamic Objects
Dynamic Objects are very similar to the standard ObjectScript object model you get when you create a new instance of a class object, but with some key differences:
It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑
1st of all for building a custom REST api service is easy by extending the %CSP.REST
Profiling CCD Documents with LEAD North’s CCD Data Profiler Ever opened a CCD and been greeted by a wall of tangled XML? You’re not alone. Despite being a core format for clinical data exchange, CCD's are notoriously dense, verbose, and unfriendly to the human eye. For developers and analysts trying to validate their structure or extract meaningful insights, navigating these documents can feel more like archaeology than engineering.
Hello Community! 👋 Welcome to the second part of the IRIS IO Utility series. This extension represents my submission for the InterSystems "Bringing Ideas to Reality" Contest 2025 and offers you an intuitive and powerful interface for importing and exporting data directly inside VS Code.
It is very easy to import CSV data into IRIS. But what if we want to preserve the original IDs in CSV?
Recently I came across with the situation when I needed to import two csv's into IRIS which were linked by one column referencing to another csv's col: a typical Foreign Key and Primary Key situation, where csv1 contains this column as Primary Key, and csv2 as Foreign key with id's related to csv1.
The image is generated by ChatGPT so don't blame it - it tried its best to generate countries as primary keys with countries.csv-cities.csv relationship :)
I'm pleased to announce the publication of gj :: dataLoader, a new VS Code extension that simplifies the task of loading data from local CSV files into SQL tables on your InterSystems IRIS servers.
Here's an introductory video:
https://www.youtube.com/embed/XohVoW5rSy4 [This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]
Hello Developers! 👋 I’m excited to introduce IRIS IO Utility, my submission for the InterSystems "Bringing Ideas to Reality" Contest 2025. This VS Code extension provides you an intuitive and powerful interface for importing and exporting data without leaving your IDE.
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