In a synchronous environment, a program execution follows a set of operations sequentially. The execution flow will start processing a step and wait for it to return a result before proceeding to the next one. With asynchronous programming, we can use the lag time required by... Read more

Python’s equivalent of a switch statement is finally here in Python 3.10. It hides behind a fancy name of Structural Pattern Matching, and does what you’d expect and more. It packs support for working with primitive data types, sequences, and even classes and enums. Today I’ll show you how... Read more

Python Constants contribute to the dynamic and updatable characteristic of the design architecture, which is essential for any coding architecture. Providing a construction with these features is related to the fact that the code blocks can be understood and developed by someone else. To meet those... Read more

Real-world data is almost always messy. And as a data scientist or analyst, you need to discover the facts about the data. To do so, the data must be tidy and free from errors. Hence, the very first step is to clean the data. Therefore, I... Read more

When it comes to feature stores, there are two main approaches to feature engineering. One approach is to build a domain-specific language (DSL) that covers all the possible feature engineering steps (e.g., aggregations, dimensionality reduction, and transformations) that a data scientist might need. The second approach... Read more

Feature stores began in the world of Big Data, with Spark being the feature engineering platform for Michelangelo (the first feature store) and Hopsworks (the first open-source feature store). Nowadays, the modern data stack has assumed the role of Spark for feature stores – feature engineering... Read more