From The Well-Grounded Python Developer by Doug Farrell
This article, excerpted from chapter 10, covers
§ Persisting Data
§ Database Systems
§ Database Structures
§ Modeling Data with SQLAlchemy
From Microservices in .NET, 2nd Edition by Christian Horsdal Gammelgaard
Let’s talk about event-based collaboration. This is an important aspect of how microservices interact with each other.
From Software Mistakes and Tradeoffs by Tomasz Lelek and Jon Skeet This article discusses the surprisingly tricky task of dealing with time and dates in your application. Take 40% off Software Mistakes and Tradeoffs by entering fcclelek into the discount… Continue Reading →
From The Jamstack Book by Raymond Camden and Brian Rinaldi
Let’s learn how to add search functionality to a Jamstack site!
From API Design Patterns by JJ Geewax
This article covers…
How the custom Purge method can be used to delete matching resources
Why the custom Purge method is dangerous and should be avoided if possible
A variety of safety precautions to avoid accidentally deleting more data than intended
How to address concerns about consistency in the matching result set
From The Well-Grounded Java Developer by Benjamin Evans, Jason Clark, and Martijn Verburg
The JDK ships with a compiler to turn Java source code into class files. Despite that fact, few projects of any size rely just on javac. Build tools are the norm for a number of reasons:
Automating tedious operations
Managing dependencies
Ensuring consistency between developers
Although many options exist, two choices dominate the landscape today: Maven and Gradle. Understanding what these tools aim to solve, digging below the surface of how they get their job done, and understanding the differences between them – and how to extend them – will pay off for the well-grounded developer.
From Feature Engineering Bookcamp by Sinan Ozdemir
This article series covers
● Recognizing and mitigating bias in our data and model
● Quantifying fairness through various metrics
● Applying feature engineering techniques to remove bias from our model without sacrificing model performance
From Feature Engineering Bookcamp by Sinan Ozdemir
This article series covers
● Recognizing and mitigating bias in our data and model
● Quantifying fairness through various metrics
● Applying feature engineering techniques to remove bias from our model without sacrificing model performance
From Feature Engineering Bookcamp by Sinan Ozdemir
This article series covers
● Recognizing and mitigating bias in our data and model
● Quantifying fairness through various metrics
● Applying feature engineering techniques to remove bias from our model without sacrificing model performance
By Christian Clausen
Flexibility is essential for long-lived codebases, however, it comes at a price. It requires effort to maintain flexibility and improperly-implemented flexibility can actually make a codebase harder to maintain. In this article, I show how good flexibility can be extracted from the structure of our code with minimal effort. But before we get into that, let’s discuss what flexibility in a codebase means.