Data Engineering Fundamentals

# Data Engineering Fundamentals

Data engineering is the foundation of any data-driven organization. Here's what you need to know.

## Pipeline Architecture

**Batch vs Stream**

- Batch: Process data in chunks (hourly, daily). Good for analytics.

- Stream: Real-time processing. Essential for live dashboards and alerts.

**ETL vs ELT**

Modern data warehouses prefer ELT—load first, transform later. More flexible and scalable.

## Data Quality

**Validation at Every Step**

- Schema validation

- Data type checking

- Completeness checks

- Freshness monitoring

## Tools and Technologies

- Apache Airflow for orchestration

- dbt for transformations

- Snowflake/BigQuery for warehousing

- Apache Spark for big data processing

Good data engineering is invisible—it just works.