Data Engineering
The practice of designing, building, and maintaining the systems and infrastructure that collect, store, and prepare data for analysis and machine learning.
Why It Matters
Data engineering is the foundation that all AI is built on. Without reliable data infrastructure, ML models have no fuel and cannot run in production.
Example
Building Apache Kafka pipelines for real-time data ingestion, Spark jobs for batch processing, and Airflow DAGs for workflow orchestration — all feeding ML pipelines.
Think of it like...
Like plumbing in a building — nobody sees it, but without it nothing works. Data engineers build the pipes that move data from where it is to where it needs to be.
Related Terms
Data Pipeline
An automated workflow that extracts data from sources, transforms it through processing steps, and loads it into a destination for use. In ML, data pipelines ensure consistent data flow from raw sources to model training.
ETL
Extract, Transform, Load — a data integration process that extracts data from source systems, transforms it into a usable format, and loads it into a destination system.
Data Lake
A centralized repository that stores vast amounts of raw data in its native format until needed. Data lakes accept structured, semi-structured, and unstructured data at any scale.
Data Warehouse
A structured, organized repository of cleaned and processed data optimized for analysis and reporting. Unlike data lakes, data warehouses store data in defined schemas.
MLOps
Machine Learning Operations — the set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently.