My team and I noticed that the %ZEN class documentation labeled all properties and methods as deprecated.
In this regard, the big question is: what will be used to replace these methods, in particular %ZEN.Auxiliary.altJSONProvider?
It is used as the basis for the MDX2JSON package, from which DeepSeeWeb gets data to build Analytical Dashboards.
Is a replacement proposed for deprecated methods of %ZEN class?
And when is this functionality scheduled to be permanently disabled?
Pandas is not just a popular software library. It is a cornerstone in the Python data analysis landscape. Renowned for its simplicity and power, it offers a variety of data structures and functions that are instrumental in transforming the complexity of data preparation and analysis into a more manageable form. It is particularly relevant in such specialized environments as ObjectScript for Key Performance Indicators (KPIs) and reporting, especially within the framework of the InterSystems IRIS platform, a leading data management and analysis solution. In the realm of data handling and
In today's digital age, effective data management and accurate information analysis are becoming essential for successful enterprise operations. InterSystems IRIS Data Platform offers two critical tools, ARCHITECT and ANALYZER, developed to deliver convenient data management.
ANALYZER is a powerful tool available within the InterSystems IRIS platform to provide extensive data analysis and visualization capabilities. This tool allows users to create summary tables and charts to analyze data. It also lets you perform detailed studies and analyses based on available data.
In today's digital age, effective data management and accurate information analysis are becoming essential for successful enterprise operations. InterSystems IRIS Data Platform offers two critical tools designed to provide convenient data management: ARCHITECT and ANALYZER.
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.
Dashboards are visual tools that combine different data in one interface for more effective monitoring and analysis. Creating a dashboard on the IRIS BI platform involves several key steps:
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.
In this article, we will analyze the intricacies of setting up tables and graphs to improve the readability of data.
Logi offers a rich set of data visualization tools. You can find anything from a wide variety of chart templates to custom CSS styles. Understanding the assortment of settings and options can be quite difficult. First, we will create a chart and a table with default settings, and give them a presentable look using InterSystems Reports (powered by Logi Report).
In this article, we will explore the use of parameters, formulas and labels in Logi Report Designer (formerly Logi JReport Designer). What are they for and how to create them?
Using the basic functionality of InterSystems Reports Designer, parameters, formulas and labels, you can significantly improve the detail and information content of the generated report. In addition, these tools allow you to automate some of the processes, which greatly speeds up and facilitates the creation of reports.
Let's analyze each tool separately now.
To connect to AtScale, we will use the SQL Server Analysis Services database. Let's open it in the Power Query editor. To do that, select Transform Data on the Home.
In the window that appears, go to the Home, expand New Source and select Analysis Services.
We offer you to embed business intelligence into your applications in order to give your users an opportunity to ask and answer sophisticated questions about their data. Typically, your application will include customizable dashboards that can provide insight into data from Business Intelligence models known as cubes.
In contrast with traditional BI systems that use static data warehouses, Business Intelligence keeps being constantly synchronized with the live transactional data.
We will use DeepSee User Portal as a dashboard builder and DeepSeeWeb - as the display of dashboards.
Today we will talk about InterSystems Reports. This is a BI system that provides you with tools to create static reports and export them to different file formats. We will see how it works using the DC Analytics public analytical sample as an example. In this article, we will examine how to familiarize yourself with the reports available in the repository, how to make a new report based on a ready-made data structure, and how to prepare a data structure from scratch.
To begin with, you need a licensed copy of InterSystems Reports.
Developers, we have prepared a tutorial to get you started with InterSystems Adaptive Analytics powered by AtScale. In it, in addition to setting up AtScale and working with data cubes, we will also touch on methods of working with InterSystems Reports and other analytical systems.
Now the course is ready and we want to conduct a pilot training course on a small group of volunteers (3-5 people).
Today we will talk about Adaptive Analytics. This is a system that allows you to receive data from various sources with a relativistic data structure and create OLAP cubes based on this data. This system also provides the ability to filter and aggregate data and has mechanisms to speed up the work of analytical queries.
Let's take a look at the path that data takes from input to output in Adaptive Analytics. We will start by connecting to a data source - our instance of IRIS.
When we work with IRIS, we usually have the ability to quickly deploy a ready-to-use infrastructure for BI (data, analytical cubes, and IRIS BI dashboards) using modules. When we start using Adaptive Analytics, we typically want to have the same functionality. Adaptive Analytics has all the tools that we need. The documentation contains a description of how to work with an open web API. All interactions between the user interface and the engine also occur via the internal web API and can be emitted.
When we are at the starting stage of BI project development, we must remember that it is crucial to select the right tool for its implementation. Today we want to show you how one of the principal functionality of dashboards is implemented in different BI systems. Let's talk about drill down from both points of view: the dashboard development, and the convenience and clarity for the end user. We will touch on the applications of this technology in IRIS BI, Power BI and Tableau.
InterSystems Developer Community analytics. Project made with InterSystems IRIS BI (DeepSee), Power BI and Logi Report Designer to visualize and analyze members, articles, questions, answers, views and other pieces of content and activity on InterSystems Developer Community.
You can see your own activity, articles and questions. Track how your contribution changes developer community.
Analyse statistics about you and your friends using IRIS BI, Adaptive Analytics, InterSystems Reports, Tableau and Power BI.
Power BI dashboards provide us with a good way to analyze aggregated information. We can even choose time periods for aggregation (you can find more details regarding it in our article about drill down). However, we might still be interested in a detailed look at specific data points. With the right data filling, we can display detailed data for any column of the chart with all filters applied to that chart. For example, we can see what exact questions were asked on the site on a particular day, who their author was, how many views they received, etc, and all of that without going beyond our
One of the most important tasks of dashboards is, on the one hand, to help you perceive data in an aggregated form, and, on the other hand, not to lose the depth of immersion in the data if you need this. One of the tools that help us achieve this result is drill down. It enables us to display several hierarchical levels of data, from aggregated to detailed.Our task is to make an analytical dashboard that will be able to drill down by time periods: from months to weeks and days in each chart. The stack that we will use for work consists of IRIS as a data storage system, Adaptive Analytics for
AtScale pulls data from the IRIS database.
The AtScale product forms a virtual OLAP cube on the intermediate layer, which external applications can access using standard SQL and MDX (Multidimensional Expressions) languages. The solution includes three main components.
When we collect temporary data (the number of purchases in the store, the number of comments on the post), it may happen that there is no data for a certain period of time. In this case, this time period (hour, day, month) is not represented in the database, that is, there is not a single row for this period. In other words, there are no rows in the database for this period.
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.
Colleagues, hello!
In this article, I will tell you how we reduced the report generation time from 28 minutes to 1 minute. Let me tell you how we achieved this.
I hope that if necessary you will be able to reproduce the same results for yourself. Useful links will be inside this post, so read to the end.
Let's start.
I was using PowerBI to create regular display data obtained from one popular web sourse with hundreds of thousands of visitors per month and a big number of users.
At the beginning of that visualisation development, I was using direct connection from Power BI to Adaptive Analytics powered by AtScale. Adaptive Analytics is useful for cached data, aggregates and fast data sources switching between development and stage phases. The “AtScale cubes'' connection method was used:
Let's assume that we have already connected a data source to Adaptive Analytics, created a project and added tables there.
In the previous article, we told you about python and dashboard, and in this article I will compare them with each other.If you are just about to get started with visualization in Python, the number of libraries and solutions will definitely amaze you:
A visualization in some libraries, such as Matplotlib, is a simple static image, which is good for explaining concepts (in a document, on slides, or in a presentation).
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.
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.
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.
Previously I had published The Article about the dataset from a real webserver, which can demonstrate how can activity and load of the Apache webserver depends on day of week, search engines indexing and some network noise.
Now I want to describe one useful function for most of webmasters and system administrators who are interested in obtaining of exactly information about visitors, hardware usage, and also about errors that gaing to their clients.
Here it is
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.
Have you heard about some restrictions in cookies for tracking visitors to your sites?
One of our clients asked me about how to know really count of visitors.
Their site works on WordPress framework, hosted on classical NAMP hosting.
They can see some statistics from Yandex Metrika conter, but we was not sure about full coverage.
We gave him the AWStats, it takes data from Apache log files on hosting.
But there were no filter of robots and hearbeat systems such as UptimeRobot or Search Engines indexing robots.
After that we decided to get data in IRIS BI for two reasons:
