One of the common challenges when dealing with data is how to send the dataset from one place to another and how to store data effectively. These problems are interconnected, as they both require serialization of data (sometimes called data encoding). Generally, the most effective data serialization method is to transform (or rather keep) data structures in binary form (opposite to text formats like JSON or XML). As usual, there are many established ways and standards for dealing with this issue.

Multitasking and multiprocessing are the two main components of parallel programming. There is native support for these features in Python (CPython). But there are some limits specific to Python that have to be considered. The main problem is called GIL (Global Interpreter Lock) - which significantly reduces the number of use cases for applications in Python (mainly for creating asynchronous pipelines).

This article explores problems related to designing and developing the system to predict renewable energy (or similar projects) - the outcome of the presented project is a web application (not a desktop app). Many things have to be considered, like the optimal technologies, team composition, budget, timetable. It might be helpful for you to read this article before you choose to develop a similar system, as otherwise, it can cost you a lot of time and money.

Cache represents a way how you can make your algorithm work faster. Basically, it is a memory that stores outputs of your algorithm for specific inputs - which outputs are cached depending on the policy. When you ask your algorithm for results, it first checks if it is stored in a cache (memory) to return without performing any computations. If this logic succeeds (information is in memory), the situation is called a cache hit. On the other hand, if the information is not in memory, it is called a cache miss.

Many helpful tools available in Python are essential for effective development - some of them are less known, others are way more popular. This article presents class methods and static methods (constructs of Python), conversion from class to dictionary (and vice versa) and metaclasses and their applications. All these things are vital for beginners and mid-senior Python engineers.

One of the traditional arguments on avoiding using ORM is the lack of performance. Although it is generally speaking correct, there are many ways to write queries in your application. Some of them are better (faster and more efficient) than others. There is almost always a way to significantly increase the performance of an application without avoiding the ORM approach. We anticipate some preliminary knowledge of Django and SQL here (so make sure you understand basic concepts before continuing).

There are many interesting use-cases for generating static websites. They reach from a specific website (like documentation) to a general-purpose application (like cached images of e-shops, blogs, etc.). The main advantage of a static website is the simplicity of the product, almost no requirements to infrastructure (even GitHub can host them) and security. Many technologies help to generate static websites, from complex frameworks to simple generators.