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.
Why It Matters
Data warehouses provide the clean, structured data needed for analytics and some ML use cases. They complement data lakes in modern data architectures.
Example
Snowflake or BigQuery storing cleaned, deduplicated customer data with consistent schemas — ready for SQL queries, dashboards, and machine learning feature engineering.
Think of it like...
Like a well-organized library with a catalog system — every book (data) has a defined place, is indexed, and can be found quickly.
Related Terms
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 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.
Data Engineering
The practice of designing, building, and maintaining the systems and infrastructure that collect, store, and prepare data for analysis and machine learning.
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.