Foundational Concepts in Data Engineering

A beginner-friendly article breaks down core data engineering concepts for moving, storing, and analyzing data at scale.

It compares batch and stream ingestion, including CDC, idempotency, and tools like Kafka, Spark Streaming, and Airflow.

The piece also explains OLTP vs. OLAP, plus how warehouses like Snowflake, BigQuery, and Redshift fit into analytics.

It covers storage choices such as row vs. columnar formats, partitioning, and formats like Parquet and ORC.