Raw Data Tables

Raw Data Tables refer to unprocessed or minimally processed tabular data structures, typically stored in formats like CSV, TSV, or database tables without transformations. They serve as the foundational source for data analysis, processing, and integration tasks in data pipelines. This concept is central to data engineering, analytics, and business intelligence workflows.

Also known as: Unprocessed Tables, Source Tables, Raw Datasets, Base Tables, Untransformed Data

Why learn Raw Data Tables?

Developers should understand Raw Data Tables when working with data ingestion, ETL (Extract, Transform, Load) processes, or data warehousing to ensure data integrity and efficient handling. They are essential in scenarios like log analysis, financial reporting, or machine learning data preparation, where raw data must be cleaned and structured before use.

Learning Resources

• course: Data Engineering Fundamentals - Raw Data Handling
• docs: Working with Raw Data in Python Pandas
• course: Introduction to ETL and Raw Data Processing
• tutorial: Raw Data Management Best Practices