Justin Thomas

A review of Martin Kleppmann’s book, “Designing Data-Intensive Applications.” Explore its coverage of foundational data systems, distributed data complexities, and derived data insights. Discover how this book bridges theory and practice, making it an essential read for both tech professionals and beginners in the field. Uncover why this book is a guiding light for creating scalable, reliable, and maintainable systems in the era of Big Data.

My first open source contribution taught me some valuable lessons. Discover how this experience can help you become a better developer.

Introduction When it comes to working with sequences of data in Python, the itertools, iterators, and generators play a significant role in enhancing efficiency and maintaining code readability. In this blog post, we’ll delve into the intricacies of these concepts and explore how they can be utilized effectively in your Python projects. Understanding Itertools and the next() Function The itertools module is a powerhouse of utility functions that simplify common tasks involving iterators....

Learn the difference between shallow copy and deep copy in Python. Explore the copy module and its copy() and deepcopy() functionsand and how to use the copy module to create shallow and deep copies of objects in Python.

Introduction to Iterators in Python. An iterator is an object in Python that represents a sequence of values. It is used to iterate over a container, such as a list, tuple, or dictionary. The iterator provides a way to access each item in the container one at a time, without having to know the details of how the container is implemented. Here’s an example of creating an iterator in Python:...