Supply Chain refers to a set of processes and activities performed by the company's business areas and its suppliers and partners (stakeholders), from the acquisition of raw materials, through production, to delivery to the end consumer. It can be better managed using SCM solutions with the orchestration of the InterSystems IRIS:

Modern data architectures utilize real-time data capture, transformation, movement, and loading solutions to build data lakes, analytical warehouses, and big data repositories. It enables the analysis of data from various sources without impacting the operations that use them. To achieve this, establishing a continuous, scalable, elastic, and robust data flow is essential. The most prevalent method for that is through the CDC (Change Data Capture)  technique. CDC monitors for small data set production, automatically captures this data, and delivers it to one or more recipients, including analytical data repositories. The major benefit is the elimination of the D+1 delay in analysis, as data is detected at the source as soon as it is produced, and later is replicated to the destination.

This article will demonstrate the two most common data sources for CDC scenarios, both as a source and a destination. For the data source (origin), we will explore the CDC in SQL databases and CSV files. For the data destination, we will use a columnar database (a typical high-performance analytical database scenario) and a Kafka topic (a standard approach for streaming data to the cloud and/or to multiple real-time data consumers).

Overview

This article will provide a sample for the following interoperability scenario:

Finishing my previous example for multiple IRIS instances, I tried
to compose a local single instance version.  The step from the external
Python app to a version using embedded Python seemed to be obvious.
This was a wrong assumption, as some Python libraries just refused installation
into my local Windows-based environment.

Introduction

With the rise of Gen AI, we believe that now users should be able to access unstructured data in a much simpler fashion. Most people have many emails that they cannot often keep track of. For example, in investment/trading strategies, professionals rely on quick decisions leveraging as much information as possible. Similarly, senior employees in a startup dealing with many teams and disciplines might find it difficult to organize all the emails that they receive. These common problems can be solved using GenAI and help make their lives easier and more organized.

Creating information dashboards, pivot tables, and widgets is an important step in analysis that provides valuable sources of information for informed decision-making. The IRIS BI platform offers many opportunities to create and customize these elements. In this article, we will take a closer look at the basic techniques for developing them and the importance of using them.

1. Dashboards:

Dashboards are visual tools that combine different data in one interface for more effective monitoring and analysis.

When analyzing data, there is often a need to look at specific indicators more thoroughly and to highlight sections of information of particular interest to a user.

 For instance, examining the data dynamics for specific regions or dates can help us uncover some hidden trends and patterns that will allow us to make an informed decision about our project in the future.

To conduct such a detailed data analysis, the most effective thing to do would be to use the "fractional analysis" method or drill-down analysis.

Released with no formal announcement in IRIS preview release 2019.4 is the /api/monitor service exposing IRIS metrics in Prometheus format. Big news for anyone wanting to use IRIS metrics as part of their monitoring and alerting solution. The API is a component of the new IRIS System Alerting and Monitoring (SAM) solution that will be released in an upcoming version of IRIS.

However, you do not have to wait for SAM to start planning and trialling this API to monitor your IRIS instances.

The following steps show you how to display a sample list of metrics available from the /api/monitor service.

In the last post, I gave an overview of the service that exposes IRIS metrics in Prometheus format. The post shows how to set up and run IRIS preview release 2019.4 in a container and then list the metrics.

This post assumes you have Docker installed. If not, go and do that now for your platform :)

Step 1. Download and run the IRIS preview in docker

Follow the download instructions at Preview Distributions to download the Preview Licence Key and an IRIS Docker image

It is very interesting to compare different BI technologies. It is curious to me what the differences are in functionality, development tools, speed and usability.

For this application, I chose a dataset with water conditions in various European countries. This is an open source dataset containing observational data from 1991 to 2017.

The team and I decided to make a model based on this BI dataset using IRIS BI, Tableau, PowerBI and InterSystems Reports (powered by Logi Reports).

For the frontend, we made a web interface in PythonFlask via Embedded Python.

Hi Community,

This post is a introduction of my open exchange iris-fhir-client application.

 iris-fhir-client can connect to any open FHIR Server by using embedded python with the help of fhirpy Library.
Get Resource information by terminal and by using CSP web application.

We are happy to share interesting information with you, as well as tell you why Python is good, where it is used.

Among the most used libraries are NumPy and Pandas. NumPy (Numerical Python) is used to sort large datasets. It simplifies mathematical operations and their vectorization on arrays. Pandas offers two data structures: Series (a list of elements) and Data Frames (a table with multiple columns). This library converts data into a Data Frame, allowing you to remove and add new columns, as well as perform various operations.

Hi, Community,

This post will demonstrate how to display data on the web by using Embedded Python , Python Flask Web Framework and Jquery datatable

We will display processes from %SYS.ProcessQuery table.

Step 1: Add table to HTML page and write below javascript code to display passed data from app.py :

HTML

  <table id="myTable" class="table table-bordered table-striped">                 
   </table>

Javascript

  <script>
    
$(document).ready(function() {
      // parse the data to local variable passed from app.py file
      let my_data = JSON.parse('{{ my_data | tojson }}');
      let my_cols = JSON.parse('{{ my_cols | tojson }}');

      $('#myTable').DataTable( {
          "data": my_data,
          "columns": my_cols,"} );
     } );

Hi Community,

This post is a introduction of my openexchange iris-python-apps application. Build by using Embedded Python and Python Flask Web Framework. 
Application also demonstrates some of the Python functionalities like Data Science, Data Plotting, Data Visualization and QR Code generation.

 Features

•  Responsive bootstrap IRIS Dashboard

•  View dashboard details along with interoperability events log and messages.

•  Use of Python plotting from IRIS

•  Use of Jupyter Notebook

•  Introduction to Data Science, Data Plotting and Data Visualization.

•  QR Code generator from python.

Maybe someday you will need to use Adaptive Analytics but there is little information about this, so I decided to write an article on how to start developing a dashboard on Tableau using the Atscale cube.

PC preparation

You need a driver to connect. I use Cloudera Hive. You can download the driver from the official site:
https://www.cloudera.com/downloads/connectors/hive/odbc/2-6-1.html
Registration is required, and you can do it right there for free. You also need to know your OS and bit depth in order to choose the right version for download.
Installation is simple, no explanation required.

I'm happy to share with the community a web server log dataset from our longtime customer, an operating company.

Their webserver operates on Apache webserver and contains data which can be useful to analyse a load and search engines activity.

After installing the project, you will get the data for a few months that can show a typical load and activity of clients, robots and also you can see how it depends on day of week, holidays and time of a day.

The Cube is also included in package.

So you can use my previous project (Promjet-Stats) and see dataset in dashboards.

Hi, everyone!

I want to share a personal project that started with a simple request at work: 

Is it's possible to know how many Caché licenses we are using? 

Reading other articles here in the community, I found this excellent article by  David Loveluck 

APM - Using the Caché History Monitor
https://community.intersystems.com/post/apm-using-cach%C3%A9-history-monitor

So, using David's article, I started using Caché History Monitor and to show all that information. 

When facing the question: Which cool tech should I use?

I regret getting a late start in the InterSystems Analytics contest. I had tried a couple of times before to use Analytics, but I had not gotten too far. I have been recording financial transactions in Excel workbook ever since I had to retire Microsoft Money. Since my iris-for-money was not fully functional, I created a worksheet per account I was tracking. I had developed a CSP page in iris-for-money to import transactions by reading a CSV file. I realized this process requires escaping many characters that can cause the process to fail.

The pandemic that struck the world in 2020 made everyone follow the news and the numbers that involve the COVID-19.

Why don’t you take that opportunity to create something simple and pleasant, to follow the number of vaccinations worldwide?

To face this challenge, I'm using the data provided by Our World in Data - Research and data to make progress against the world’s largest problems.

They have a dedicated repository on Github with the data of COVID-19, and I took the vaccination data to help me with my tracker.

If you didn't know the guys, check it out, it well worth your time.

Hi everyone,

It's the bottom of the 9th inning, but I have a few more plays to make in our technical World Series 

InterSystems IRIS Business Intelligence provides the Cube Registry as an interface for managing and scheduling build and synchronize tasks for your cubes. The Cube Event Monitor is a new tool on Open Exchange to help you keep track of those events' status and performance, the number of records being updated, and any build errors (errors when processing individual records) that have occurred.

Hi Developers and Interface Engineers!

I'd like to share with you how you can help your organization today obtain a better understanding of key message processing metrics (i.e. average message processing times, number of inbound messages, number of outbound messages, etc.)! Given the embedded IRIS database powering integration, you already have all the data you need -- you just need to put the data to use and present them in a user-friendly format!

Now available on Open Exchange is a library of third party charts available to use within DeepSee/InterSystems IRIS BI dashboards. To start, simply download and install, select the new portlet as the widget type, then select the chart type that you desire. If you don't find the type of chart you are looking for, you can easily extend the portlet to implement your desired chart type. These new chart types can be used within existing dashboards or you can create new dashboards using them.

Hi everyone,

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

This error is sometimes seen while viewing a listing in InterSystems IRIS Business Intelligence:
ERROR #5540: SQLCODE: -99 Message: User <USERNAME> is not privileged for the operation (4)

As the error suggests, this is due to a permission error. To figure out which permissions are missing/needed, we can take a look at the SQL query that is generated. We will use a query from SAMPLES as an example.

SELECT TOP 1000 %ID, DateOfSale, Outlet->City, %EXTERNAL(Channel) AS Channel, Product->Name AS Product, UnitsSold, AmountOfSale AS Revenue, (Discount * 100) || '%' AS Discount, Comment FROM HoleFoods.

Hello Community,

Thank you all for your continued feedback and support of our ad hoc reporting platform, VDM.  There's been some questions around setting up a non-ODBC connection for InterSystems platforms.  We published a new YouTube video showing the steps necessary to connect to InterSystems Caché and InterSystems IRIS with BridgeWorks VDM. 

In the previous part of this series, we saw how to include data in a portlet from within DeepSee. This used the built in data controller. In this part, we are going to be pulling in data from outside of DeepSee. This will include both information from within InterSystems IRIS and from the OS.

Why use this?

This is useful if you would like to create a dashboard that only contains information about your system. It is also useful if you want to display data about your system along side data that you have stored in DeepSee.

What will we learn?

What is %SQLRESTRICT

%SQLRESTRICT is a special %FILTER clause for use in MDX queries in InterSystems IRIS Business Intelligence. Since this function begins with %, it means this is a special MDX extension created by InterSystems. It allows users to insert an SQL statement that will be used to restrict the returned records in the MDX Result Set. This SQL statement must return a set of Source Record IDs to limit the results by. Please see the documentation for more information.

Why is this useful?

This is useful because there are often times users want to restrict the results in their MDX Result Set based on information that is not in their cubes. It may be the case that this information may not make sense to be in the cube. Other times this can be useful when there is a large set of values you want to restrict. As mentioned before, this is not a standard MDX function, it was created by InterSystems to handle cases were queries were not performing well or cases that were not easily solved by existing functions.

In the previous part of this series, we saw how to reference a web page that will enhance our dashboard experience.  Now we will look into referencing data that is already in our cubes.

In this example, we will be referencing the controller object and we will be extracting data from it. This data will then be displayed as text in our Dashboard. In Part 5, we will show how to incorporate this data into other charting libraries.

Why use this?

The controller object contains data from an MDX result set.