#Best Practices

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Best Practices recommendations on how to develop, test, deploy and manage solutions on InterSystems Data Platforms better. 

Article Yuri Marx · May 5, 2025 10m read

RabbitMQ is a message broker that allows producers (those who send a data message) and consumers (those who receive a data message) to establish asynchronous, real-time, and high-performance massive data flows. RabbitMQ supports AMQP (Advanced Message Queuing Protocol), an open standard application layer protocol. 
The main reasons to employ RabbitMQ include the following:

  • You can improve the performance of the applications using an asynchronous approach.
  • It lets you decouple and reduce dependencies between services, microservices, and applications with the help of a data message mediator, meaning that there is no need for producers and consumers of exchanged data to know each other.
  • It allows the long-running processing of sent data (with the results) to be delivered after utilizing a response queue.
  • It helps you migrate from monolithic to microservices, where microservices exchange data via Rabbit in a decoupled and asynchronous way.
  • It offers reliability and resilience by making it possible for messages to be stored and forwarded. A message can be delivered multiple times until it is processed.
  • Message queueing is the key to scaling your application. As the workload increases, you will only have to add more workers to handle the queues faster.
  • It works well with data streaming applications.
  • It is beneficial for IoT applications.
  • It is a must for Bots’ communication.
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Article Ben Schlanger · May 7, 2025 4m read

Here at InterSystems, we often deal with massive datasets of structured data. It’s not uncommon to see customers with tables spanning >100 fields and >1 billion rows, each table totaling hundred of GB of data. Now imagine joining two or three of these tables together, with a schema that wasn’t optimized for this specific use case. Just for fun, let’s say you have 10 years worth of EMR data from 20 different hospitals across your state, and you’ve been tasked with finding….

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Article Jose Ruperez · Apr 28, 2025 2m read

Sometimes customers need a small IRIS instance to do something in the cloud and shut it down, or they need hundreds of containers (i.e. one per end user or one per interface) with small workloads. This exercise came about to see how small an IRIS instance could be. For this exercise we focused on what is the smallest amount of memory we can configure for an IRIS instance. Do you know all the parameters that affect the memory allocated by IRIS ?

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Article Andreas Schneider · Apr 22, 2025 4m read

When using standard SQL or the object layer in InterSystems IRIS, metadata consistency is usually maintained through built-in validation and type enforcement. However, legacy systems that bypass these layers—directly accessing globals—can introduce subtle and serious inconsistencies.

Understanding how drivers behave in these edge cases is crucial for diagnosing legacy data issues and ensuring application reliability.
The DATATYPE_SAMPLE database is designed to help analyze error scenarios where column values do not conform to the data types or constraints defined in the metadata.

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Article sween · May 14, 2025 7m read

Real Time FHIR® to OMOP Transformation

This part of the OMOP Journey,  we reflect before attempting to challenge Scylla on how fortunate we are that InterSystems OMOP transform is built on the Bulk FHIR Export as the source payload.  This opens up hands off interoperability with the InterSystems OMOP transform across several FHIR® vendors, this time with the Google Cloud Healthcare API.

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Article sween · Apr 23, 2025 6m read

Nearline FHIR® Ingestion to InterSystems OMOP from AWS HealthLake

This part of the OMOP Journey, we reflect before attempting to challenge Scylla on how fortunate we are that InterSystems OMOP transform is built on the Bulk FHIR Export as the source payload.  This opens up hands off interoperability with the InterSystems OMOP transform across several FHIR® vendors, including Amazon Web Services HealthLake.

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