Data Engineering is the practice of designing and building systems for collecting, storing and analyzing data at scale. Data Engineers acts as architects of a company's data infrastructure, building the pipelines that transform raw, messy data into clean, accessible formats for data scientists and analysts.

Understanding core concepts behind data engineering is important before working with tools such as Apache Kafka, Spark, Airflow, Hadoop or cloud platforms. This article explains most important foundational concepts in a beginner-friendly and practical way.

1. Batch vs Streaming Ingestion

Data Ingestion is the process of collecting and importing data into a system.

There are two main approaches: