Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, notably using significant whitespace
Since I saw many posts on Developer Community related to Python, and the very good articles and application written by @Eduard Lebedyuk I was wondering: "As a Object Script developer, why would I want to use an other language in Object Script?
Continuing the description of work on the use of the openhl python module in productive mode.
Since the version of iris with Embedded Python, does not yet have a final release, it is already necessary to use it in production now. We decided to back up the service for exporting requests to a xlsx file on a separate server, and save the query result in a global in a separate database.
For a long time, we have been using a utility in production to export the result of a query to an Excel spreadsheet. Moreover, we have applied a modification of it, in which the explicit setting of column formats is a priority.
Anton Umnikov Sr. Cloud Solutions Architect at InterSystems AWS CSAA, GCP CACE
AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores.
Embedded python gives the option to load and run python code in the InterSystems IRIS server. You can either use library modules from Python pip, like numpy, pandas, etc, or you can write your own python modules in the form of standalone py files.
So once you are happy with the development phase of the IRIS Embedded Python solution there is another very important question of how the solution could be deployed.
One of the options you can consider is using the ZPM Package manager which is described in this article.
Since I started to use internet (late 90's), I always had a CMS (content management system) present to make easier post
any information in a blog, social media or even an enterprise page. And later years putting all my code into github I
used to document it on a markdown file. Observing how easy could be persisting data into Intersystems IRIS with the
Native API I decided to make this application and force myself to forget a little of SQL and stay open to key-value database
model.
This is a first attempt to use Embedded Python in IRIS The Python code is adapted from solutions for Advent of Code 2020 contest. Test data are all input to my personal challenge.
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.
This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data into an IRIS database instance via SQL syntax, for demo purpose.
This is the third post of a series explaining how to create an end-to-end Machine Learning system.
Training a Machine Learning Model
When you work with machine learning is common to hear this work: training. Do you what training mean in a ML Pipeline?
Training could mean all the development process of a machine learning model OR the specific point in all development process
that uses training data and results in a machine learning model.
This is my introduction to a series of posts explaining how to create an end-to-end Machine Learning system.
Starting with one problem
Our IRIS Development Community has several posts without tags or wrong tagged. As the posts keep growing the organization
of each tag and the experience of any community member browsing the subjects tends to decrease.
First solutions in mind
We can think some usual solutions for this scenario, like:
This is the second post of a series explaining how to create an end-to-end Machine Learning system.
Exploring Data
The Intersystem IRIS already has what we need to explore the data: an SQL Engine! For people who used to explore data in
csv or text files this could help to accelerate this step. Basically we explore all the data to understand the intersection
(joins) which should help to create a dataset prepared to be used by a machine learning algorithm.
Sometimes you need quickly and easily import data into IRIS. For this, an IRIS import manager has been developed.
This application allows you to import JSON data and also provides a really simple interface for transferring data from MongoDB collections to IRIS globals. It has never been so easy.
In this article I'm going to show how to integrate the InterSystems IRIS Database with Python to serve a Machine
Learning Model of Natural Language Processing (NLP).
How to use the IRIS Native API in Python to access globals and plot some charts.
Why Python?
With a large adoption and use in the world, Python have a great community and a lot of accelerators | libraries to deploy any kind of application.
If you are curious (https://www.python.org/about/apps/)
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Extracting and plotting pButtons data including timeframes and iostat.
This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
Execute arbitrary Python code
Seamlessly transfer data from InterSystems IRIS into Python
Build intelligent Interoperability business processes with Python Interoperability Adapter
Save, examine, modify and restore Python context from InterSystems IRIS
Other articles
The plan for the series so far (subject to change).
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.
This extension allows you to browse and edit InterSystems IRIS BPL processes as jupyter notebooks.
This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
This series of articles would cover Python Gateway for InterSystems Data Platforms. Execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
Since version 2019.2, InterSystems IRIS has provided their Native API for Python as a high-performance data access method. The Native API allows you to directly interact with the native IRIS data structure.
This is the second post on the resources for Developers. This part is about Open Exchange
Using Open Exchange to Learn InterSystems
InterSystems Open Exchange is a applications gallery of tools, connectors, and libraries which InterSystems Developers submit to share the experience, approaches and do business. All the applications are either built with InterSystems data platforms or are intended to use for development with InterSystems data platforms.
If you are a beginner developer you can take a look at applications in Technology Example category. All the applications in this category come with open source code repositories, so you are able to run the samples and examples in a docker container with IRIS on your laptop or in the cloud IRIS sandbox. Examples:
This series of articles would cover Python Gateway for InterSystems Data Platforms. Leverage modern AI/ML tools and execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:
Hi all. Today we are going to upload a ML model into IRIS Manager and test it.
Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, Python 3.6.5.
Introduction
These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools.
By update
Open Exchange app
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