Data Engineering

What is Apache Kafka? Learn how it works, why it’s used in modern data engineering, and whether you need it, with clear metaphors and beginner-friendly guidance.

Learn what dbt (data build tool) is, how it helps data teams transform data using SQL, and whether you actually need to use it in your stack.

Learn how to build a real-time data pipeline using Apache Kafka. Includes Docker setup, Python producer/consumer code, and architecture explained step-by-step.

Wondering whether to build your AWS infrastructure manually or with Terraform? Learn the pros, cons, and best practices for data engineers working with AWS services like S3, Lambda, Glue, and Step Functions.

Explore the top data engineering trends of 2025 and how the field has evolved from traditional ETL to real-time, AI-powered, cloud-native pipelines. Learn what modern data teams must know.

Learn what idempotency means in data engineering, why it matters for reliable pipelines, and how to implement it using real-world examples and modern tools.

Learn how to design scalable data engineering pipelines in 2025 with modern tools, architectural patterns, and real-time best practices for reliability and growth.

Learn the key differences between ETL and ELT, when to use each, and which tools are best for cloud-native data pipelines.

New to data engineering? Discover the best ETL tools for beginners with free tiers, no-code features, and real-world examples. Start your first pipeline today!

Learn what ETL means, why it's essential in data engineering, and how to build a simple ETL pipeline in Python using real-world data.