Data Engineering

In this article, we take a look at the key skills required of a data engineer in 2025.

This article delves into the intricacies of data pipelines, a critical aspect of modern data management and processing. By exploring the fundamental concepts, design principles, and practical implementation strategies, the reader will gain a comprehensive understanding of how data pipelines function and how they can be effectively utilized in various applications.

Introduction As applications become more data-driven, RESTful APIs have emerged as a popular way to build interfaces that enable diverse client apps to interact with backend data and services. Well-designed REST APIs power the data backends of web, mobile, IoT, and other applications. They provide a standardized way to expose data and functionality over HTTP…

Database normalization is the process of organizing data in a database to reduce data redundancy and improve data integrity. This practical guide covers the basics of normalization, including the different normal forms such as 1NF, 2NF, and 3NF.

Data sharding is a fundamental technique in modern database management, providing the means to enhance system performance, scalability, and reliability. This article aims to explore the core principles and practices of data sharding, illuminating the pathway to effective data distribution.

Data virtualization is a software layer that allows applications to access data from various sources without requiring the data to be moved or copied. It connects data consumers with data sources in real-time. The article provides an introduction to data virtualization concepts, benefits, use cases, architectures, and leading products.

This article provides a checklist of steps and considerations when deploying a data engineering project to production, covering infrastructure setup, testing, monitoring and more. Following this checklist will help ensure a smooth deployment and transition to production systems.

Platform engineering distinguishes itself through a systematic approach towards designing, building, and maintaining platforms, providing a solid foundation for multiple applications and services.

In this article, we delve into an overview of OLTP and OLAP, explore their key differences, use cases, and offer insights into when one should be chosen over the other.