CompTIA Data+ (DA0-002)Data MiningMedium
A data engineer is designing an ETL process to integrate data from a legacy system into a new data warehouse. The legacy system stores customer addresses in a single free-form text field, while the new data warehouse requires separate fields for street, city, state, and zip code. Which data transformation technique is most appropriate for handling this specific data structure change?
- ANormalization
- BFiltering
- CParsing
- DAggregation
Show answer & explanationAnswer & explanation
Correct answer: C. Parsing
Parsing is the most appropriate technique as it involves breaking down a complex data field into multiple, structured components. This directly addresses the need to separate the single address field into distinct street, city, state, and zip code fields.
Why the other options are wrong
- A. Normalization typically refers to organizing data to reduce redundancy and improve data integrity, not directly splitting a single field.
- B. Filtering involves selecting a subset of data based on criteria, which doesn't change the data's structure.
- D. Aggregation involves summarizing data, which is not applicable here.
Data Parsing
The process of breaking down a complex string or text field into multiple, structured components based on defined patterns, delimiters, or rules.
- Used to extract meaningful information from unstructured or semi-structured data.
- Commonly applied to address fields, log files, or free-form text.
- Often involves regular expressions or specific parsing functions.
Memory trick: Transforming data is like a puzzle, parsing pieces into place.