Hi guys!

I'd like to present you my new project: iris-analytics-notebook, a notebook approach to use IRIS analytics capabilities.

Project description

In past few years, notebooks tools like Jupyter are gaining popularity due its natural way to express ideias.

An almost unipresent tool for data scientists, notebook can also help to improve the impact of analytics tools for all sort of users.

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About this article:

In InterSystems IRIS, the default form of access to the management portal is HTTP, which means that if the client is in the office and the server is in the cloud, many clients probably desire to encrypt their traffic in some way.

Thus, we would like to show you some ways to encrypt your traffic to and from the IRIS management portal (or various REST services) running on AWS.

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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).

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Hi community, I used website-analyzer - an app that uses InterSystems NLP and Crawler4J to extract all website content and do NLP on it. I limited to 200 pages and discovered this:

Top 10 Concepts - business and content topics in the InterSystems site:

Other frequency concepts, see the focus in the iris speed, scale and data value:

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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.

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Article
· Dec 7, 2020 6m read
IRIS Python Native API in AWS Lambda

If you are looking for a slick way to integrate your IRIS solution in the Amazon Web Services ecosystem, server less application, or boto3 powered python script, using the IRIS Python Native API could be the way to go. You don't have to build out to far with a production implementation until you'll need to reach out and get something or set something in IRIS to make your application do its awesome sauce, so hopefully you will find value in this article and build something that matters or doesn't matter at all to anybody else but you as that is equally important.

image

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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.

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Article
· Nov 20, 2020 2m read
Enrich your analytics projects with NLP

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:

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How can you allow computers to trust one another in your absence while maintaining security and privacy?

“A Dry Martini”, he said. “One. In a deep champagne goblet.”
“Oui, monsieur.”
“Just a moment. Three measures of Gordons, one of vodka, half a measure of Kina Lillet. Shake it very well until it’s ice-cold, then add a large thin slice of lemon peel. Got it?”
"Certainly, monsieur." The barman seemed pleased with the idea.
Casino Royale, Ian Fleming, 1953


OAuth helps to separate services with user credentials from “working” databases, both physically and geographically. It thereby strengthens the protection of identification data and, if necessary, helps you comply with the requirements of countries' data protection laws.

With OAuth, you can provide the user with the ability to work safely from multiple devices at once, while "exposing" personal data to various services and applications as little as possible. You can also avoid taking on "excess" data about users of your services (i.e. you can process data in a depersonalized form).

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Hello developers!

I submitted for the contest a solution that would allow organizing message notifications and providing information to users of the messenger Telegam. This is achieved by creating two bots: an administrator and an informant. The administrator manages the list of users connected to the bot informator and their access rights.

The Solution based on project
https://github.com/intersystems-community/TelegramAlerts

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My team is working on redesigning an Interoperability solution that currently runs on a HealthShare server that is part of a mirror. Most of the messages are delimited records processed using Complex Record Mapping. We were told to utilize cloud services available in AWS, use containers, autoscaling…

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The productions are components developed with InterSystems IRIS Interoperability module to promote integrations between systems, data sources/targets, web services, API, message channels, etc. Productions are composed by:

1) Business services to get/ingest/get data events or requests;

2) Business operations to send or persist data to repositories, systems, API, web services, etc; and

3) BPL - BPEL flows to orchestrate, mediate, compose and route data from business services to business operations.

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Article
· Jun 19, 2020 5m read
Migrate from Java Business Host to PEX

Migrate from Java Business Host to PEX

With the release PEX in InterSystems IRIS 2020.1 and InterSystems IRIS for Health 2020.1, customers have a better way to build Java into productions than the Java Business Host. PEX provides a complete set of APIs for building interoperability components and is available in both Java and .NET. The Java Business Host has been deprecated and will be retired in a future release.

Advantages of PEX

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Those of you who are following the FullStack competition here in the Developer Community will know that I submitted an entry named qewd-conduit. I wanted to summarise why I think it's something worth you taking a bit of time to check out.

qewd-conduit uses the Node.js-based QEWD framework alongside IRIS to implement the back-end REST APIs for something known as the RealWorld Conduit application:

https://github.com/gothinkster/realworld

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$LIST string format and %DynamicArray and %DynamicObject classes

IRIS, and previously Cache, contain several different ways to create a sequence containing a mixture of data values. A data sequence that has been available for many years is the $LIST string. Another more recent data sequence is the %DynamicArray class, which along with the %DynamicObject class, is part of the IRIS support for JSON string representation. These two sequences involve very different tradeoffs.

$LIST String Format

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Article
· Oct 7, 2016 4m read
Forwarding Requests in a REST Service

One useful feature of our REST framework is the ability for a dispatch class to identify request prefixes and forward them to another dispatch class. This approach of modularizing your URL map will improve code readability, enable you to easily maintain separate versions of an interface, and provide a means to protect API calls that only certain users will be allowed to access.

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