Converting data from raw formats into structured, cleaned, and enriched forms through cleaning, filtering, aggregating, joining, and normalizing
by Databricks Staff
Data transformation is the process of taking raw data that has been extracted from data sources and turning it into usable datasets. Data pipelines often include multiple data transformations, changing messy information into clean, quality, trusted data that organizations can use to meet operational needs and create actionable insights. The data transformation process is a key process in data engineering.
The data transformation process is critical for realizing value from raw data. Organizations collect vast amounts of data that may include inconsistencies, missing values or duplicates. By cleaning and transforming datasets, leaders can be assured they are making decisions based on trustworthy, timely and accurate data.
Data transformation helps improve data quality by enhancing its compatibility and usability. By standardizing formats to ensure consistency across different data sources, it becomes easier for leaders to analyze and integrate quality data into their decisions.
