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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OAuth server to be deployed on the IRIS learning cloud platform. Clients - one on the other instance of the learning IRIS server, the other client locally on my computer in the container docker.

Both clients get a seemingly correct link (through ##class(%SYS.OAuth2.Authorization).GetAuthorizationCodeEndpoint()) to the login request form:  

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Article
Dmitrii Kuznetsov · Mar 31, 2019 20m read
How to write the home address right?

How Tax Service, OpenStreetMap, and InterSystems IRIS
could help developers get clean addresses

 

Pieter Brueghel the Younger, Paying the Tax (The Tax Collector), 1640

 

In my previous article, we just skimmed the surface of objects. Let's continue our reconnaissance. Today's topic is a tough one. It's not quite BIG DATA, but it's still the data not easy to work with: we're talking about fairly large amounts of data. It won't all fit into RAM at once, and some of it won't even fit on the drive (not due to lack of space, but because there's a lot of junk). The name of our subject is FIAS DB: the Federal Information Address System database - the databases of addresses in Russia. The archive is 5.5 GB. And it's a compressed XML file. After extraction, it will be a full 53 GB (set aside 110 GB for extraction). And when you start to parse and convert it, that 110 GB won't be enough. There won't be enough RAM either.

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Headache-free stored objects: a simple example of working with InterSystems Caché objects in ObjectScript and Python

Neuschwanstein Castle

Tabular data storages based on what is formally known as the relational data model will be celebrating their 50th anniversary in June 2020. Here is an official document – that very famous article.  Many thanks for it to Doctor Edgar Frank Codd. By the way, the relational data model is on the list of the most important global innovations of the past 100 years published by Forbes.

On the other hand, oddly enough, Codd viewed relational databases and SQL as a distorted implementation of his theory.  For general guidance, he created 12 rules that any relational database management system must comply with (there are actually 13 rules). Honestly speaking, there is zero DBMS's on the market that observes at least Rule 0. Therefore, no one can call their DBMS 100% relational :) If you know any exceptions, please let me know.

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