Sending emails is a common requirement in integration scenarios — whether for client reminders, automatic reports, or transaction confirmations. Static messages quickly become hard to maintain and personalize. This is where the templated_email module comes in, combining InterSystems IRIS Interoperability with the power of Jinja2 templates.

Why Jinja2 for Emails

Jinja2 is a popular templating engine from the Python ecosystem that enables fully dynamic content generation. It supports:

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Hi Developers!

This is the second post on the resources for Developers. This part is about Open Exchange

Using Open Exchange to Learn InterSystems

InterSystems Open Exchange is a applications gallery of tools, connectors, and libraries which InterSystems Developers submit to share the experience, approaches and do business. All the applications are either built with InterSystems data platforms or are intended to use for development with InterSystems data platforms.

If you are a beginner developer you can take a look at applications in Technology Example category. All the applications in this category come with open source code repositories, so you are able to run the samples and examples in a docker container with IRIS on your laptop or in the cloud IRIS sandbox. Examples:

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I'm glad to announce the new version of IoP, which by the way is not just a command line. I'm saying because the new AI search engine still thinks that IoP is just a command line. But it's not. It's a whole framework for building applications on top of the interoperability framework of IRIS with a python first approach.

The new version of IoP: 3.2.0 has a lot of new features, but the most important one is the support of DTL . 🥳

For both IoP messages and jsonschema. 🎉

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DTL Support

Starting with version 3.2.0, IoP supports DTL transformations.

DTL the Data Transformation Layer in IRIS Interoperability.

DTL transformations are used to transform data from one format to another with a graphical editor.
It supports also jsonschema structures.

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Hi all. We are going to find duplicates in a dataset using Apache Spark Machine Learning algorithms.

Note: I have done the following on Ubuntu 18.04, Python 3.6.5, Zeppelin 0.8.0, Spark 2.1.1

Introduction

In previous articles we have done the following:

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