#Dashboards

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.

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?

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.

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.

How are we doing THIS year versus the same period LAST year? 
This is a common need in Business Intelligence. In fact, many design specifications for reports make use of a comparison between a selected period (year, quarter, etc) up to a certain date (for example November 15th, 2016) and a summary of the same information for the previous year (i.e. up to November 15th, 2015). 
This post shows how to implement this in DeepSee.

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.

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.

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 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.

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!

Hi everyone,

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

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.

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.

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.

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.

Why log in to view pivot tables when you can have them delivered directly to your inbox? Are you in need of automated reporting for InterSystems IRIS Business Intelligence? Pivot Subscriptions is your solution: a tool for subscribing to scheduled email reports of pivot tables inside InterSystems IRIS Business Intelligence (previously known as DeepSee).

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.

What is a portlet?

The simple answer is: a custom widget. A portlet can exist by itself on a DeepSee dashboard, it can be used along side standard DeepSee widgets, or along side other portlets. The rendering of the custom widget is completely user defined. This means you can embed a web page, create a form to perform any sort of action needed based on the data on your dashboard, use third party charting libraries, or simply display data from outside of a DeepSee cube.

How to get started

I've asked a lot of questions leading up to this, so I wanted to share some of my progress.

The blue line represents the number of messages processed.  The background color represents the average response time.  You can see ticks for each hour (and bigger ticks for each day).   Hovering over any point in the graph will show you the numbers for that period in time.

This is super useful for "at a glance" performance monitoring as well as establishing patterns in our utilization.

Here is the query used:

SELECT
    mh.name                                  AS MessageType,
    COUNT(mh.

Introduction

A calculated measure is a powerful feature in DeepSee and can help to enrich your analyzes.  In the case of complex or long running computations plugins can be useful. This article shows with a simple example how you can build and use a plugin in DeepSee.

Hi everyone,

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

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.

In the previous part of this series, we saw how to define a basic portlet. Now we will look into making this portlet reference a web page that will enhance our dashboard experience.

In this example, we will be embedding a Developer Community article along side a couple of widgets displaying information related to the number of views on the Developer Community articles. This example is not hosted on the Community Analytics server, but if it was we could see the view counts going up as we interacted with the page.

Why use this?

In a real case, perhaps you have an embedded page from an external web site showing the current Emergency Room wait times for Hospitals in your area. This portlet can be used along side widgets from your Emergency Room showing how many people are waiting, how many doctors are active, and how many people are being treated. As other Emergency Room wait times grow, you can possibly expect your volume to increase as well. This can help you make decisions on how to allocate resources.

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.

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.

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?

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.

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.

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.

Presenter: André Cerri
Task: Use third-party visualization tools to present your DeepSee data
Approach: Use DeepSee REST services to access DeepSee data from third-party tools
 

Come see examples of how you can use popular 3rd party data visualization tools to access your DeepSee data.

Content related to this session, including slides, video and additional learning content can be found here.