In this article, we will explore the development of an IRIS client for consuming RESTful API services that have been developed to the OData API standard.

We will be exploring a number of built-in IRIS libraries for making HTTP requests, reading and writing to JSON payloads, and seeing how we can use them in combination to build a generic client adaptor for OData. We will also explore the new JSON adapter for deserializing JSON into persistent objects.

Monitor incremental changes in the database through scheduled tasks, display change trends through charts, set alarm thresholds, and write information to messages.log

How to use it

You can install it through Docker or ZPM

Deploying with Docker Prerequisites

Make sure you have git and Docker desktop installed.

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.

Inevitably, though, you’ll need to make changes.

Kong provides an open source configuration management tool (written in Go), called decK (which stands for declarative Kong)

Hey Developers,

The InterSystems Multi-Model Contest is over. Thank you all for participating in our exciting coding marathon!

And now it's time to announce the winners!

A storm of applause goes to these developers and their applications:

Challenges of real-time AI/ML computations

We will start from the examples that we faced as Data Science practice at InterSystems:

• A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
• An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
• A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

Hey Developers,

Watch as the presenter uses IKO to deploy the WebGateway, SAM, and InterSystems API Manager with the latest version of the InterSystems Kubernetes Operator:

⏯ IKO: The InterSystems Kubernetes Operator (Version 3.0)

https://www.youtube.com/embed/KRUMYz6eGhE
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In addition to the new supported platforms (Ubuntu 22.04 LTS and RHEL 9), Community Edition limits will soon be updated to:

Such most popular nowadays CI platforms as GitHub and Gitlab, offers the ability to run any docker image as a service, mostly useful for integration tests.

And I could define a GitHub workflow like this for instance, for some of my Python project which, requires the connection to IRIS

Taking advantage of the Quiniela ML application and as we indicated in the previous article, we are going to explain how we can perform a JWT authentication between our frontend developed in Angular and our backend developed in InterSystems IRIS.

I remind you of the architecture of our QuinielaML project:

In the previous article, we saw different modules in IRIS AI Studio and how it could help explore GenAI capabilities out of IRIS DB seamlessly, even for a non-technical stakeholder. In this article, we will deep dive into "Connectors" module, the one that enables users to seamlessly load data from local or cloud sources (AWS S3, Airtable, Azure Blob) into IRIS DB as vector embeddings, by also configuring embedding settings like model and dimensions.

In the preceding section, we explored the installation process and initiated the writing of the IRIS in native Python. We will now proceed to examine global traversal and engage with IRIS class objects.

get: this function is used to get values from the traversal node.

Using SQL Gateway with Python, Vector Search, and Interoperability in InterSystems Iris

Part 2 – Python and Vector Search

Since we have access to the data from our external table, we can use everything that Iris has to offer with this data. Let's, for example, read the data from our external table and generate a polynomial regression with it.

For more information on using python with Iris, see the documentation available at https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=AFL_epython

Let's now consume the data from the external database to calculate a polynomial regression. To do this, we will use a python code to run a SQL that will read our MySQL table and turn it into a pandas dataframe:

Hi everyone,

The project IRIS History Monitor received an update, using ZPM and the built-in REST API /api/monitor/metrics.