I worked through the Community for proposals to provide end users
in an easy way with data formatted as EXCEL sheet.

There is a great article Tips & Tricks - SQL to Excel

there's an important message embedded: "EXCEL can interpret HTML tables and display them as usual"

Hello everyone,

I was first introduced to TDD almost 9 year ago, and I immediately fell in love with it.
Nowadays it's become very popular but, unfortunately, I see that many companies don't use it. Moreover, many developers don't even know what it is exactly or how to use it, mainly beginners.

Last time we deployed a simple IRIS application to the Google Cloud. Now we’re going to deploy the same project to Amazon Web Services using its Elastic Kubernetes Service (EKS).

We assume you’ve already forked the IRIS project to your own private repository. It’s called <username>/my-objectscript-rest-docker-template in this article. <root_repo_dir> is its root directory.

Before getting started, install the AWS command-line interface and, for Kubernetes cluster creation, eksctl, a simple CLI utility. For AWS you can try to use aws2, but you’ll need to set aws2 usage in kube config file as described here.

Hi, Community!

Since this article is an overview of Flask Login, let's begin with Flask Introduction!

What is Flask?

In the realm of web development, Python has emerged as a formidable force, offering its versatility and robustness to create dynamic and scalable applications. For that reason, tools and services compatible with this language are in demand these days. Flask is a lightweight and easy-to-use web framework for Python. It stands out as a lightweight and user-friendly option. Its simplicity and flexibility have made it a popular choice for developers, particularly for creating smaller-scale applications. It is based on the Werkzeug toolkit and provides a simple but powerful API for building web applications.
Unlike its full-stack counterparts, Flask provides a core set of features, focusing on URL routing, template rendering, and request handling. This minimalist approach makes Flask lightweight and easy to learn, allowing developers to build web applications quickly and without the burden of unnecessary complexity.

When I describe InterSystems IRIS to more technically-minded people, I always start with how it is a multimodel DBMS at its core.

In my opinion that is its main advantage (on the DBMS side). And the data is stored only once. You just choose the access API you want to use.

A short post for now to answer a question that came up. In post two of this series I included graphs of performance data extracted from pButtons. I was asked off-line if there is a quicker way than cut/paste to extract metrics for mgstat etc from a pButtons .html file for easy charting in Excel.

See: - Part 2 - Looking at the metrics we collected

Just wanted to share my Zabbix template for monitoring InterSystems IRIS on Linux servers.

It monitors irisusr (configurable) memory consumption:

Introduction

Since version 2019.2, InterSystems IRIS has provided their Native API for Python as a high-performance data access method. The Native API allows you to directly interact with the native IRIS data structure.

Application to import 12 Datasets along with 43 tables dynamically by using LOAD DATA command which loads data from a source into an IRIS SQL table.
List of Datasets

Hi Developers!

Often I find questions on how to install IRIS, connect to IRIS from IDE, setup the environment, compile, debug, maintain the repository.

Here below possibly the shortest way to set up all the environment and start development with ObjectScript on InterSystems IRIS.

Prerequisites

Make sure you have Git, Docker, and VSCode installed

Install Docker and ObjectScript extensions into VSCode

Sign in or Create an account on Github

Here we go!

Introduction

Data analytics is a crucial aspect of business decision-making in today's fast-paced world. Organizations rely heavily on data analysis to make informed decisions and stay ahead of the competition. In this article, we will explore how data analytics can be performed using Pandas and Intersystems Embedded Python. We will discuss the basics of Pandas, the benefits of using Intersystems Embedded Python, and how they can be used together to perform efficient data analytics.

Introduction

Nowadays, there is a lot of applications that are using Open Authorization framework (OAuth) to access resources from all kinds of services in a secure, reliable and efficient manner. InterSystems IRIS is already compatible with OAuth 2.0 framework, in fact, there is a great article in the community regarding OAuth 2.0 and InterSystems IRIS in the following link here.

In the previous parts (1, 2) we talked about globals as trees. In this article, we will look at them as sparse arrays.

A sparse array - is a type of array where most values assume an identical value.

In practice, you will often see sparse arrays so huge that there is no point in occupying memory with identical elements. Therefore, it makes sense to organize sparse arrays in such a way that memory is not wasted on storing duplicate values.

In some programming languages, sparse arrays are part of the language - for example, in J, MATLAB. In other languages, there are special libraries that let you use them. For C++, those would be Eigen and the like.

Globals are good candidates for implementing sparse arrays for the following reasons: