Data Engineering & Pipelines

Data Engineering is the foundation that makes ML possible. It involves building and maintaining the infrastructure and pipelines that collect, store, transform, and serve data for analysis and model training.

Core concepts include ETL/ELT pipelines (Extract-Transform-Load patterns, Apache Spark, Airflow, dbt), data warehousing (Snowflake, BigQuery, Redshift, star/snowflake schemas), data quality (validation, profiling, anomaly detection, great expectations), and Change Data Capture (CDC for real-time data synchronization).

For ML engineers, data engineering skills are crucial: understanding data modeling (star schema, data vault), stream processing (Kafka, Kinesis), data versioning, and building reliable feature pipelines that bridge raw data to ML-ready features. The quality of your data pipeline directly determines the quality of your models.