Data Engineering Fundamentals

OLTP is a normalized row-store for fast point writes; OLAP is a denormalized column-store star schema for big scans, and you separate them so analytics never slows transactions.

The lake is cheap raw flexible storage, the warehouse is clean governed fast SQL, and the lakehouse puts warehouse reliability on top of lake storage so you get both in one place.

A data lake cheaply stores all raw data for later; a data warehouse stores clean, modelled data ready to query fast.

A data pipeline is an orchestrated assembly line that moves data from source to destination, transforming it reliably at each stage.

Data engineering builds the reliable plumbing that turns messy raw data into clean, query-ready data for everyone else.

Databricks is the managed, optimized, cloud platform around Apache Spark built by Spark's own creators, adding notebooks, Delta Lake, governance, and ML tooling so you run Spark without operating it.

ETL is the assembly line that pulls raw data out, cleans and reshapes it, and files it into a system others can query.

Schema-on-write enforces structure before storing for clean, fast queries; schema-on-read stores raw and applies structure at read time for maximum flexibility.

OLTP is the fast transactional counter; OLAP is the heavy analytics back office, and you keep them separate so reports never freeze live operations.

Evolve schemas safely with versioned formats, a registry, and compatibility rules; use columnar Parquet so analytics prunes columns, pushes down filters, and compresses hard.