Data Engineering Articles

Understand how Apache Spark processes data at scale—from its foundational components to the advanced features driving modern big data workflows.

This guide explores integration testing strategies, tools, and best practices to help you build reliable, high-performing software systems.

Master the technical interview process with these essential questions covering algorithms, system design, and behavioral scenarios. Get expert answers, code examples, and proven preparation strategies.

This post breaks down the complex architecture of Hadoop into clear, digestible components—ideal for data professionals seeking to understand how it enables scalable, fault-tolerant big data processing.

Learn what database sharding is, how it works, how it differs from partitioning and replication, and what strategies you can use for sharding.

This guide explains data partitioning in simple terms, covering types, use cases, tools, and implementation strategies to help optimize database performance.

Explore what a data lake is, how it fits into modern data architecture, and how it enables scalable, flexible, data-driven strategies.

This practical guide to Apache Airflow 3.0 explores its features, improvements, and everything you need to know about the most significant update yet.

This post demystifies sharding and partitioning, helping you decide which method to use for scaling databases efficiently. Learn key concepts, examples, and tools.

Learn how to choose the right AWS certification and confidently prepare as a data engineer.

Understand the difference between Git and GitHub, how they work together in modern workflows, and when to use each for solo and team projects.

Are you applying for a job that requires Scala knowledge? Discover the top basic, intermediate, and advanced Scala questions to ace your job interview!