#InterSystems IRIS

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InterSystems IRIS is a Complete Data Platform
InterSystems IRIS gives you everything you need to capture, share, understand, and act upon your organization’s most valuable asset – your data.
As a complete platform, InterSystems IRIS eliminates the need to integrate multiple development technologies. Applications require less code, fewer system resources, and less maintenance.

At hackathons that InterSystems participated and I supported, many students were asking how all their teammates could use the same IRIS database that they spun up in a container. I suggested using ngrok to expose their localhost IRIS and realized we don't have documentation on that. Hence, I thought this would be great to let more people knwo about this powerful technique for enhancing collaboration during development and testing.

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Hey, community! 👋

We are a team of Stanford students applying technology to make sense of climate action. AI excites us because we know we can quickly analyze huge amounts of text.

As we require more reports on sustainability, such as responsibility reports and financial statements, it can be challenging to cut through the noise of aspirations and get to the real action: what are companies doing

That’s why we built a tool to match companies with climate actions scraped from company sustainability reports.

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The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below.
Note: I classified each answer by the assertiveness that I consider as good, average and bad.

Note 2: The article has 4 parts, each one for an exam area.

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In your Interoperability Production you could always have a Business Operation that is an HTTP client, that uses OAuth 2.0 for authentication, but you had to customize the Operation for this authentication methodology. Since v2024.3, which was lately released, there is a new capability, providing new settings, to handle this more easily.

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The first part of this article provides all the background information. It also includes links to the DATATYPE_SAMPLE database, which you can use to follow along with the examples.

In that section, we explored an error type ("Access Failure") that is easy to detect, as it immediately triggers a clear error message when attempting to read the data via the database driver.

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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:

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InterSystems IRIS Data Platform is a comprehensive, multi-model, multi-workload data platform that is ideal for accommodating the challenging requirements of applications for the Internet of Things. It is a complete platform for developing, executing, and maintaining IoT applications in a single, consistent, unified environment. It features a distributed architecture to support massive data-ingest rates and data volumes, while providing the flexibility and durability of an enterprise-grade transactional multi-model database to ingest, process, and persist data from a wide range of devices in different formats. It features a complete set of integration, event-processing, and integrated analytics capabilities, including full SQL support and text processing, business process orchestration, and a standards-based development environment.

Connect to, ingest, and persist a wide range of disparate device data types and formats

The data types associated with IoT applications are often heterogeneous, as they may originate from various devices with diverse functions and manufactured by different vendors. The underlying data platform must be able to ingest and process a wide range of raw data types in their original formats. Many applications also require the data platform to persist all of the disparate source data to detect deviations from normal ranges, accommodate downstream ad hoc analytics, maintain regulatory compliance, and fulfill other purposes.

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If you're migrating from Oracle to InterSystems IRIS—like many of my customers—you may run into Oracle-specific SQL patterns that need translation.

Take this example:

SELECT (TO_DATE('2023-05-12','YYYY-MM-DD') - LEVEL + 1) AS gap_date
FROM dual
CONNECT BY LEVEL <= (TO_DATE('2023-05-12','YYYY-MM-DD') - TO_DATE('2023-05-02','YYYY-MM-DD') + 1);

In Oracle:

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Last Chapter: Creating a REST client to get Tracks from Spotify REST API - Part4 Save the Search Result

Git link: https://github.com/ecelg/InterSystems-IRIS-as-a-Spotify-REST-client

OK.... based on what I have done.... I am able to

1. Query Track information by making use of the Spotify API

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Deploying new IRIS instances can be a time-consuming task, especially when setting up multiple environments with mirrored configurations.

I’ve encountered this issue many times and want to share my experience and recommendations for using Ansible to streamline the IRIS installation process. My approach also includes handling additional tasks typically performed before and after installing IRIS.

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Last Chapter: Creating a REST client to get Tracks from Spotify REST API - Part3 Get some data (e.g. Artists)

Git link: https://github.com/ecelg/InterSystems-IRIS-as-a-Spotify-REST-client

OK we create a method to get data and lets try to get some Tracks 😁

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Introduction

Businesses often use in-memory databases or key-value stores (caching layers) when applications require extremely high performance. However, in-memory databases incur a high total cost of ownership and have hard scalability limits, incurring reliability problems and restart delays when memory limits are exceeded. In-memory key-value stores share these limitations and introduce architectural complexity and network latency as well.

This article explains why InterSystems IRIS™ data platform is a superior alternative to in-memory databases and key-value stores for highperformance SQL and NoSQL applications.

Taking Performance and Efficiency to the Next Level

InterSystems IRIS is the only persistent database that can match or beat the performance of in-memory databases and caching layers for concurrent data ingestion and analytics processing. It can process incoming transactions, persist the data to disk, and index it for analytics in under one microsecond on commercially available hardware without introducing network latency.

The superior ingest performance of InterSystems IRIS results in part from its multi-dimensional data engine, which allows efficient and compact storage in a rich data structure. Using an efficient, multi-dimensional data model with sparse storage techniques instead of two-dimensional tables, random data access and updates are accomplished with very high performance, fewer resources and less disk capacity. It also provides in-memory, in-process APIs in addition to traditional TCP/IP access APIs to optimize ingest performance.

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This will be a short article about Python dunder methods, also known as magic methods.

What are Dunder Methods?

Dunder methods are special methods in Python that start and end with double underscores (__). They allow you to define the behavior of your objects for built-in operations, such as addition, subtraction, string representation, and more.

Some common dunder methods include:

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Using Flask, REST API, and IAM with InterSystems IRIS

Part 2 – Flask App

Flask is a web development microframework written in Python. It is known for being simple, flexible, and enabling rapid application development.

Installing Flask is very simple. Once you have python installed correctly on your operating system, we need to install the flask library with the pip command. For REST API consumption, it is advisable to use the requests library. The following link provides a guide to installing flask: https://flask.palletsprojects.com/en/stable/installation/

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The new version of InterSystems Testing Manager which I released last week brings @Timothy Leavitt's cool
Test Coverage Tool to VS Code, and is my entry for the 2025 Developer Tools contest.

Here's a teaser screenshot showing how the unit tests of the IPM project don't yet cover a feature that apparently allows an IPM repository to override its sort order.

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Recently, I replaced my old laptop with a new one and had to migrate all my data. I was looking for a guide but couldn’t find anything that explained in detail how to migrate server connections from InterSystems Studio and Visual Studio Code from one PC to another. Simply reinstalling the tools is not enough, and migrating all the connections manually seemed like a waste of time. In the end, I managed to solve the problem, and this article explains how.

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IRIS supports CCDA and FHIR transformations out-of-the-box, yet the ability to access and view those features requires considerable setup time and product knowledge. The IRIS Interop DevTools application was designed to bridge that gap, allowing implementers to immediately jump in and view the built-in transformation capabilities of the product.

In addition to the IRIS XML, XPath, and CCDA Transformation environment, the Interop DevTools package now provides:

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Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

  • 🧠 Python + smolagents to orchestrate the agent’s “brain”
  • 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

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Article
· Mar 28 2m read
Reviews on Open Exchange - #50

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.
The rating reflects the experience of the reviewer with the status found at the time of review.
It is kind of a snapshot and might have changed meanwhile.
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.
Some were accepted and merged, and some were just ignored.
So if you made a major change and expect a changed review just let me know.

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Article
· Dec 9, 2024 40m read
Reviews on Open Exchange - #Rollup

In principle I'm looking in forward direction.
Though from time to time it makes sense to turn back and see the full path taken.

  • I started reporting my reviews almost 4 years ago.
  • I just reviewed packages that I could reproduce in my environment.
  • Skipping items beyond my physical or mental capacities.
  • So some great packages might not be present here
  • But I added also 139 contributions of other reviewers
  • It was anyhow a total of 769 reviews published
  • Meeting over this time 191 different authors

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