Before we start talking about databases and different data models that exist, first we'd better talk about what a database is and how to use it.

A database is an organized collection of data stored and accessed electronically. It is used to store and retrieve structured, semi-structured, or raw data which is often related to a theme or activity.

At the heart of every database lies at least one model used to describe its data. And depending on the model it is based on, a database may have slightly different characteristics and store different types of data.

To write, retrieve, modify, sort, transform or print the information from the database, a software called Database Management System (DBMS) is used.

The size, capacity, and performance of databases and their respective DBMS have increased by several orders of magnitude. It has been made possible by technological advances in various areas, such as processors, computer memory, computer storage, and computer networks. In general, the development of database technology can be divided into four generations based on the data models or structure: navigational, relational, object and post-relational.

If you need write your organization Data Architecture and map to the InterSystems IRIS, consider following Data Architecture Diagram and references to the intersystems iris documentation, see:

Summary

A Quick start to include InterSystems IRIS Tables in Data Build Tool using Python.

It uses the sqlalchemy plugin with sqlalchemy-iris which enables the iris strategy for duckdb as a source for a dbt project.

Process-private Globals can be used as a data global in storage definition. That way, each process can have its own objects for the class with ppg storage. For example lets define a pool, which can:

• add elements to a pool (ignoring duplicates)
• check if an element exists in the pool

Here's the class:

Hi,

In many projects I was faced with storing hierarchical data (tree) in classes.
By tree, I mean such data, where each node has a parent node — an object of the same class.
Many examples of such data can be given. For example, a catalog in the online store. Suppose that this online store sells books, in this case, the category tree might look like this:

Hi everyone,

I want to talk about our project and use the dataset theme for this contest.

Our intention never was to be a data curator, especially because sometimes my precious data means a lot for me, but not for the rest of the world.

We want to go a step further and empower the user to find the perfect dataset for their needs.

Our project is a bridge between the data science community and the developer's community using InterSystems IRIS to achieve this mission.

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This data set demonstrates a basic M:N relationship between 2 tables
The dataset is targeted to show a slim implementation of M:N
It's no question that other implementations exist. But at significantly more storage consumption.

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.