How do you balance the need to achieve an early success with SOA against the requirement for an architecture that will deliver long term success? You don't want to get bogged down in architectural committees for three years, but you don't want to make short term decisions that will be roadblocks to long term success.

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If you're deploying to more than one environment/region/cloud/customer, you will inevitably encounter the issue of configuration management.

While all (or just several) of your deployments can share the same source code, some parts, such as configuration (settings, passwords) differ from deployment to deployment and must be managed somehow.

In this article, I will try to offer several tips on that topic. This article talks mainly about container deployments.

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As you probably know, InterSystems IRIS has a mechanism for auto-generating methods. So, when you create an index in a class, you create methods that make your work easier. There is also a good article on Community that describes such methods.

Take, for example, IndexNameOpen(val), where IndexName is the name of the corresponding index. This method returns an object in which the value of this index corresponds to the value of val.

But this method is available only for unique indexes.

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Today, is important analyze the content into portals and websites to get informed, analyze the concorrents, analyze trends, the richness and scope of content of websites. To do this, you can alocate people to read thousand of pages and spend much money or use a crawler to extract website content and execute NLP on it. You will get all necessary insights to analyze and make precise decisions in a few minutes.

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This article is a follow-up to the previous one on how to migrate from popular databases (like PostgreSQL and MySQL) to IRIS.
We will use the same procedures utilized to migrate from PostgreSQL. However, you will see that it is even easier since the data types in MySQL are very similar to IRIS. That is why we will not need to create transformation rules in the columns.

Get the sample data to the migration process

In GitHub it is possible to download a docker-compose project to build and run 2 databases:

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This is the third post of a series explaining how to create an end-to-end Machine Learning system.

Training a Machine Learning Model

When you work with machine learning is common to hear this work: training. Do you what training mean in a ML Pipeline?
Training could mean all the development process of a machine learning model OR the specific point in all development process
that uses training data and results in a machine learning model.

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GlobalSummit too close now, so many people going to be there from so many companies. I'm sure that somebody already uses Docker or even Kubernetes in their work, I do. And would like to share my experience and thoughts about what could be better. And want to hear other people about their experience, how you use Docker, what issues have you faced and how did you solve it. I think InterSystems will help us to find time and place when we could do it, and hope @Luca Ravazzolo will join us.

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Requirement: Transform source XML message to target JSON.

step1: First create json equivalent xml from any online tool.

step2: use add in  studio utility create persistent classes from json equivalent xml and compile it , now target persistent classes are ready

step3: Do the above step for source xml or xsd and generate persistent classes for source and compile it

step4: complete the DTL by selecting root node from source, target persistent classes and complete the mappings and compile it

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I recently started to study interoperability and I found the official documentation very helpful in understanding how it works, though I still had some trouble implementing it myself. With the help I got from my coworkers, I managed to create a Demo of a system and learn through practice. Because of that, I decided to write this to help others with "getting their hands dirty" and share the help I got.

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Hi,

I have wrote an article about how to install the intersystems cache driver in a Docker container, and then deploy it using Azure Functions: 

How to run a (Python) Azure Function as a Docker container & Deploy it using Bicep | Victor Sanner

This might be useful to others, especially the dockerfile which I have copied below. This builds a debian docker container and installs the Intersystems Cache driver, which python can then use :)

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If you create your own Language Extensions to Object you mostly have to find the
proper %ZLANGC00 or %ZLANGV00 or %ZLANGF00 and add the extensions manually.
A few utilities do it already automatically (ZPM, ZME, ..)
This utility allows you to add your extensions programmatically. (eg. at first run, or during installation)
I found this quite useful for my Docker-based demos as it all happens at the start time.

It is related to these articles

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Introduction

This document is intended to provide a survey of various High Availability (HA) strategies that can be used in conjunction with InterSystems Caché, Ensemble, and HealthShare Foundation. This document also provides an overview of the various types of system outages that can occur, as well as how each strategy would handle a given outage, with the goal of helping you choose the right strategy for your specific deployment.

The strategies surveyed in this document are based on three different HA technologies:

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This is the fourth part of the series of articles on migrating from the main databases on the market to InterSystems IRIS. In this part, the procedures for migrating from Microsoft SQL Server will be detailed. As described in previous articles, there are currently a few options to do the migration, but the two most popular options include the use of DBeaver (https://openexchange.intersystems.com/package/DBeaver) or SQLGateway.

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About regulations

Personal data privacy regulations have become an indispensable requirement for projects dealing with personal data. The compliance with these laws is based on 4 principles:

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Article
David Loveluck · Sep 28, 2015 1m read
DTL TechFAQ

Ensemble is based on message flow, and a data transformation is a way to convert from one message type to another. DTL (Data Transformation Language) adds a layer to this - it provides a graphical way to do the conversion. This is really helpful because most of the time, people with domain-specific knowledge may not have extensive coding skills. However, you always have the ability to do some coding, so if you need or want to, this is available.

DTL has several components: the data transformation engine, the language itself, and the DTL editor.

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In this article, we’ll look at one of the ways to monitor the InterSystems IRIS data platform (IRIS) deployed in the Google Kubernetes Engine (GKE). The GKE integrates easily with Cloud Monitoring, simplifying our task. As a bonus, the article shows how to display metrics from Cloud Monitoring in Grafana

Note that the Google Cloud Platform used in this article is not free (price list), but you can leverage a free tier. This article assumes that you already have a project in the Google Cloud Platform (referred to as <your_project_id>) and have permission to use it. 

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QEWD is assumed by most people to only integrate with IRIS (or Cache) via a connection through IRIS's high-performance C interface.  This requires QEWD (and its Node.js environment) to be installed and configured on the same machine as IRIS.

I'm frequently asked if QEWD can run on a separate server (or servers), and access IRIS (or Cache) over a network connection.  The answer is yes it can, but the information on how to set it up in this way has been admittedly a bit tricky to discover.

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