Data Transformation
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.
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.
- Data 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.
