In the previous article, we looked at one way to create a custom operator that manages the IRIS instance state. This time, we’re going to take a look at a ready-to-go operator, InterSystems Kubernetes Operator (IKO). Official documentation will help us navigate the deployment steps.

For the benefit of those who want to use the Document Database (DocDB) capabilities within InterSystems IRIS, and specifically the REST API it provides, I put together a Postman Collection that provides samples for several basic calls.

For example:

Hi InterSystems Community!

The ObjectScript language of InterSystems IRIS has the ability to extend classes using a very interesting feature called XData.

It is a section in your class that can be used to create custom definitions to be used within the class itself and also externally.

To create one or more XData definitions for your class is very easy, see the example:

Tiny web application that allows you to anonymize CSV files. Based on InterSystems IRIS®

Version: store/intersystems/iris-community:2020.3.0.221.0

Application

Sample CSV where Date of Birth and Sex will remain untouched, while the rest should be anonymized.

The application recognizes the header columns and allows the user to chose which ones to ignore.

Hi guys.

That time I'll show you a way to use a machine learning model implemented using IntegradeML as source for an IRIS analytics cube dimension.

Creating the ML model:

Execute this SQL in order to create a new ML model using IntegratedML SQL extension:

The InterSystems IRIS has two major paths to a digital service: API/Web Service into Interoperability module and multimodel Database/Analytics. Each of them has your security configuration.

To do API security you apply an OAuth or JWT plug-in to the API endpoint. So in the Admin Portal, API producer and consumers get the keys to authenticate the API and consume it. The Admin Portal allows you configure RBAC policies too.

Hi everyone,

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

Today, is important analyze the content into portals and websites to get informed, analyze the concorrents, analyze trends, the richness and scope of content of websites. To do this, you can alocate people to read thousand of pages and spend much money or use a crawler to extract website content and execute NLP on it. You will get all necessary insights to analyze and make precise decisions in a few minutes.

I compiled some data about past InterSystems DC constest. Some statistics:

• 64 applications (17% of all current apps into OEX - 377 apps in december/2020)
• 08 contests
• 29 developers

See the summary:

Web Crawling is a technique used to extract root and related content (HTML, Videos, Images, etc.) from websites to your local disk. This is allows you apply NLP to analyze the content and get important insights. This article detail how to do web crawling and NLP.

To do web crawling you can choose a tool in Java or Python. In my case I'm using Crawler4J. (https://github.com/yasserg/crawler4j).

QEWD is assumed by most people to only integrate with IRIS (or Cache) via a connection through IRIS's high-performance C interface. This requires QEWD (and its Node.js environment) to be installed and configured on the same machine as IRIS.

I'm frequently asked if QEWD can run on a separate server (or servers), and access IRIS (or Cache) over a network connection. The answer is yes it can, but the information on how to set it up in this way has been admittedly a bit tricky to discover.

As we all well know, InterSystems IRIS has an extensive range of tools for improving the scalability of application systems. In particular, much has been done to facilitate the parallel processing of data, including the use of parallelism in SQL query processing and the most attention-grabbing feature of IRIS: sharding. However, many mature developments that started back in Caché and have been carried over into IRIS actively use the multi-model features of this DBMS, which are understood as allowing the coexistence of different data models within a single database. For example, the HIS qMS database contains both semantic relational (electronic medical records) as well as traditional relational (interaction with PACS) and hierarchical data models (laboratory data and integration with other systems). Most of the listed models are implemented using SP.ARM's qWORD tool (a mini-DBMS that is based on direct access to globals). Therefore, unfortunately, it is not possible to use the new capabilities of parallel query processing for scaling, since these queries do not use IRIS SQL access.

Meanwhile, as the size of the database grows, most of the problems inherent to large relational databases become right for non-relational ones. So, this is a major reason why we are interested in parallel data processing as one of the tools that can be used for scaling.

In this article, I would like to discuss those aspects of parallel data processing that I have been dealing with over the years when solving tasks that are rarely mentioned in discussions of Big Data. I am going to be focusing on the technological transformation of databases, or, rather, technologies for transforming databases.

According IDC, 80% of all data produced are NoSQL. See:

There are digital documents, scanned documents, online and offline texts, blob content into SQL, images, videos and audio. Imagine a Corporate Analytics initiative without all these data to analyze and support decisions?

In all the world, many projects are using techonologies to transform these NoSQL data into textual content, to allows analyze it. See: