What are some common pitfalls in data transformation?
An example of an AI chatbot conversation about common data transformation pitfalls.
What are some common pitfalls in data transformation?
Some common pitfalls include data loss, incorrect data types, and overlooking null values. Would you like to know more about any specific issue?
Yes, can you explain how to avoid data loss during transformation?
To avoid data loss, always back up your data before transformation, and validate the output against the original dataset.
What about handling null values?
Handling null values can be done by either imputing them with a default value or removing records with nulls, depending on your analysis needs.
Thanks! Are there tools that can help with these transformations?
Yes, tools like Apache Spark, Pandas, and Talend can help streamline data transformation processes and minimize pitfalls.
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