Detailing Python 3.12.4 Release Updates Introduction With the release of Python 3.12.4 in June 2024, the Python community welcomes an array of exciting new features and critical fixes. This version is packed with enhancements that improve security, performance, and usability, solidifying Python’s reputation as a powerful and versatile programming language. Whether you’re a seasoned developer […]
Introduction: The rising demand for energy, fueled by factors such as the adoption of electric vehicles, the constant construction of new data centers, and the increased use of electric devices, is presenting significant challenges to the modern energy grid in terms of capacity, reliability, and adaptability. OpenADR (Open Automated Demand Response) is a protocol developed […]
First things first, what is data quality monitoring? Data quality monitoring for machine learning can generally be thought of from two perspectives. One perspective is that of traditional data-engineering. This type of monitoring is concerned with the “physical” characteristics of the data and ensuring they are what you expect them to be. It involves criteria […]
The final installment of our blog series on AWS testing methodologies focuses on integration testing. This crucial phase ensures that all components of your application work together seamlessly in a live environment, simulating real-world usage with production code and test data. Below is an outline designed to guide the creation of a comprehensive and informative […]
A deep dive into functional testing for AWS development Introduction In our exploration of advanced testing techniques for AWS development, we’ve delved into powerful tools like moto for unit testing and pytest.mark.parametrize for enhancing test coverage and efficiency. Building on this foundation, we turn our focus to a pivotal tool that bridges the gap between […]
Leveraging Moto and Pytest Introduction In the world of AWS development, ensuring the reliability, efficiency, and correctness of your cloud-based applications is paramount. As cloud solutions grow increasingly complex, so too does the challenge of effectively testing these systems. Traditional testing methods often fall short in the face of AWS’s vast and intricately interconnected services. […]
Introduction In the rapidly evolving field of data engineering, maintaining high-quality, reliable, and efficient data pipelines is crucial for businesses to make informed decisions and stay competitive. One methodology that has been instrumental in achieving these objectives is Test-Driven Development (TDD). At its core, TDD involves a simple, yet powerful cycle: write a failing test […]
Python Performance: Issue 2 – Feature Envy Previous Issue Recap In the previous issue we discussed the differences between the “Clean Code” version of calculating the cumulative area of a collection of shapes and “the old fashioned way”. Robert Martin, aka “Uncle Bob”, advocates for a “clean” polymorphic approach to the problem, where each shape […]
Python Performance: Issue 1 – The Polymorphism Rule Welcome to Python Performance Welcome to the Python Performance blog series. In this series, I will be exploring various performance topics in Python, with the aim to create a list of heuristics to help developers write more performant Python code before they ever start thinking about reaching […]
This series of blog posts will illustrate how to use DBT with Azure Databricks: set up a connection profile, work with python models, and copy noSQL data into Databricks(from MongoDB). In the first part, we will talk about how to set up a profile when using dbt-databricks python package. Install python package dbt-databricks using pip […]