Simulating Early Molecular Processes for Educational Insights Introduction What if you could simulate the very origins of life on Earth? While we can’t rewind the clock billions of years, we can use the power of computation to model the processes that may have sparked the first living cells. At the heart of this mystery are […]
Streamlined UX, Powerful Tools, and AI Integration Introduction Having powerful and intuitive tools is crucial for success. Databricks has recently unveiled the next generation of its Notebooks, bringing a host of new features designed to enhance productivity and ease of use. This update includes a modernized user interface, advanced Python capabilities, and AI-powered authoring tools, […]
Exploring Particle Physics Simulations in Python Editors note: At New Math Data we strive to regularly push the envelope, test our skills, and generally innovate, in addition to a deep commitment to our more traditional practices such as analytics and data warehousing. It is in that spirit that we present this article, an experiment in […]
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 […]
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