Python
In today’s rapidly evolving technological landscape, the need for handling multiple databases is more pressing than ever. Whether to enhance redundancy, optimize performance, test correctness of the new solution or cater to varied data storage requirements, employing multiple databases can offer significant advantages.
Many applications work with large amounts of data, often exceeding 1 million records in the database. Loading larger datasets into a database then introduce overheads to development velocity, impacting both delivery costs and speed. Here’s how to minimize these costs.
Fitness functions are useful for tracking software architecture metrics. Here’s how to use them in Python.
A bit of functional Python will help you discover alternate approaches to how programs can be structured and why it’s important to practice such skills on small, well-defined programs.
Mocking up real services is a no-brainer, no one wants tests to affect the production environment in any way. Here’s how to do that with AWS Cognito in Python.
Recently, more and more Python applications have been built based on async communication using the asyncio library that allows a programmer to write concurrent code with async/await syntax. This tutorial shows how to connect to the PostgreSQL database within an asynchronous environment using SQLAlchemy and asyncio.
