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Answer: Identify and resolve duplicate data, handle missing data or null values by filling them with default values, and convert data types using SQL or PySpark.
Option C is the most comprehensive approach. It involves a thorough data cleansing process that includes identifying and resolving duplicates, handling missing data or null values appropriately, and ensuring data types are correct, which is crucial for accurate data analysis.
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You are tasked with implementing a data cleansing process for a large dataset that contains customer information for a multinational corporation. The dataset includes fields such as customer ID, name, address, phone number, and email. Describe the steps you would take to ensure the data is clean, including how you would handle duplicate data, missing data, or null values, and how you would convert data types where necessary.
A
Ignore missing data and null values, focus only on converting data types.
B
Remove all rows with missing data or null values, then convert data types.
C
Identify and resolve duplicate data, handle missing data or null values by filling them with default values, and convert data types using SQL or PySpark.
D
Delete the entire dataset if any missing data or null values are found.