Data Transformation is the process of converting data from one format, structure, or value set into another to make it suitable for analysis, integration, or storage. It is a key step in ETL (Extract, Transform, Load) processes, ensuring data consistency and usability.
Whether converting JSON to CSV, standardizing date formats, or aggregating monthly sales totals — transformation is the bridge between raw data and business-ready insight.
Types of Data Transformation
Structural Transformation
Changing data format, schema, or organization (e.g., JSON to CSV).
Data Cleansing
Correcting errors, removing duplicates, and handling missing values.
Normalization & Standardization
Converting data into a consistent format (e.g., date formats, currency).
Aggregation & Summarization
Combining multiple data points for analysis (e.g., monthly sales totals).
Data Enrichment
Adding relevant external information (e.g., geolocation, customer demographics).
Steps in Data Transformation
- Data Extraction — Retrieving data from source systems.
- Data Cleaning & Preprocessing — Fixing inconsistencies and preparing data.
- Data Conversion — Changing formats, units, or structures.
- Data Mapping — Aligning source fields with target fields.
- Data Validation & Testing — Ensuring accuracy, integrity, and consistency.
- Loading & Deployment — Storing transformed data in the target system.