What is meant by Data transformation?
The term "data transformation" refers to the process of converting data from one format, structure, or system to another. This often occurs in the context of data migrations, data integration, or analysis processes to make the data usable for processing, storage, or analysis. Data transformation is an essential step to ensure that data is consistent, relevant, and correct when transferred into the target system.
Typical software functions in the area of "data transformation":
- Data Cleansing: Removing duplicate, inaccurate, or faulty data to ensure the quality of the data.
- Data Format Conversion: Converting data into a different format, e.g., from CSV to XML.
- Data Enrichment: Adding external information to existing datasets to complete or improve them.
- Data Aggregation: Summarizing data from various sources or across different time periods.
- Data Validation: Checking the data for consistency, completeness, and adherence to specific rules or standards.
- Data Mapping: Assigning fields or data elements from a source to corresponding fields in the target system.
- Data Filtering: Hiding or removing irrelevant or unwanted data from the data stream.
- Automated Transformation: Using rules or algorithms to automate recurring transformation processes.
Examples of "data transformation":
- Database Conversion: Migrating data from a relational database system (e.g., MySQL) to a NoSQL system (e.g., MongoDB).
- Data Migration: Transferring data from an old ERP system to a new one, including adjustments to new fields and data formats.
- Customer Data Cleansing: Removing duplicate entries and correcting incorrect address data in a CRM system.
- Sensor Data Integration: Aggregating and transforming real-time sensor data into a big data analytics tool.
- Format Conversion: Converting XML data into JSON for API communication.
- Log File Cleansing: Filtering out irrelevant information from log files for security event analysis.