The InterSystems IRIS is a great data platform and it is met the current features required by the market. In this article, you see the top 10:

Note: this list was updated because many features are added to IRIS in last 3 years (thanks @Kristina.

In today's data landscape, businesses encounter a number of different challenges. One of them is to do analytics on top of unified and harmonized data layer available to all the consumers. A layer that can deliver the same answers to the same questions irrelative to the dialect or tool being used. InterSystems IRIS Data Platform answers that with and add-on of Adaptive Analytics that can deliver this unified semantic layer. There are a lot of articles in DevCommunity about using it via BI tools.

Power BI

Connecting to a Data Source

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.

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.

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.

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.

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.

Parameters are individually configurable variables.

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.

Report

We are reporting for a company using Adaptive Analytics and InterSystems Reports Server. Previously, this report was generated in the form of screenshots from DeepSee. In general, it was not bad, but it took a lot of time and did not look very readable.

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.

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.

In IRIS BI, cubes initially provide hierarchies according to which drilldown will later occur.

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.

When we start building analytical reports based on data from IRIS or Adaptive Analytics cubes, a visualization problem appears.

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.

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.

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.

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.

Design Center is used for designing OLAP cubes, forming links between metadata and dimensions of a virtual cube. Along with the task of designing a data schema, the issues of access policy to certain data and security are also solved here.

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.

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.