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
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:
Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.
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 project was thought of when I was thinking of how to let Python code deal naturally with the scalable storage and efficient retrieving mechanism given by IRIS globals, through Embedded Python.
My initial idea was to create a kind of Python dictionary implementation using globals, but soon I realized that I should deal with object abstraction first.
So, I started creating some Python classes that could wrap Python objects, storing and retrieving their data in globals, i.e., serializing and deserializing Python objects in IRIS globals.
This post is a introduction of my openexchange iris-python-apps application. Build by using Embedded Python and Python Flask Web Framework. Application also demonstrates some of the Python functionalities like Data Science, Data Plotting, Data Visualization and QR Code generation.
Features
Responsive bootstrap IRIS Dashboard
View dashboard details along with interoperability events log and messages.
This is the first article of a series diving into visualization tools and analysis of time series data. Obviously we are most interested in looking at performance related data we can gather from the Caché family of products. However, as we'll see down the road, we are absolutely not limited to that. For now we are exploring python and the libraries/tools available within that ecosystem.
import grongier.pex
import iris
import MyResponse
class MyBusinessOperation(grongier.pex.BusinessOperation):
def OnInit(self):
print("[Python] ...MyBusinessOperation:OnInit() is called")
self.LOGINFO("Operation OnInit")
return
def OnTeardown(self):
print("[Python] ...MyBusinessOperation:OnTeardown() is called")
return
def OnMessage(self, messageInput):
if hasattr(messageInput,"_IsA"):
if messageInput._IsA("Ens.StringRequest"):
self.LOGINFO(f"[Python] ...This iris class is a Ens.StringRequest with this message {messageInput.StringValue}")
self.LOGINFO("Operation OnMessage")
response = MyResponse.MyResponse("...MyBusinessOperation:OnMessage() echos")
return response
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.
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.
Keywords: Anaconda, Jupyter Notebook, Tensorflow GPU, Deep Learning, Python 3 and HealthShare
1. Purpose and Objectives
This "Part I" is a quick record on how to set up a "simple" but popular deep learning demo environment step-by-step with a Python 3 binding to a HealthShare 2017.2.1 instance . I used a Win10 laptop at hand, but the approach works the same on MacOS and Linux.
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 a public announcement for the first release of Intersystems Cache Object-Relational Mapper in Python 3. Project's main repository is located at Github (healiseu/IntersystemsCacheORM).
About the project
CacheORM module is an enhanced OOP porting of Intersystems Cache-Python binding. There are three classes implemented:
The intersys.pythonbind package is a Python C extension that provides Python application with transparent connectivity to the objects stored in the Caché database.
In last week's discussion we created a simple graph based on the data input from one file. Now, as we all know, sometimes we have multiple different datafiles to parse and correlate. So this week we are going to load additional perfmon data and learn how to plot that into the same graph.
Since we might want to use our generated graphs in reports or on a webpage, we'll also look into ways to export the generated graphs.
The reason is that ActiveState Python version 2.7.X is built with Visual Studio 2008 and Microsoft provides Visual Studio 2008, which one must install, so that Python C extensions can be built.
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.
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.
Jupyter Notebook is an interactive environment consisting of cells that allow executing code in a great number of different markup and programming languages.
To do this Jupyter has to connect to an appropriate kernel. There was no ObjectScript Kernel, that is why I decided to create one.
Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python.
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