What are the main challenges in flattening data?
An example of an AI chatbot conversation about challenges in flattening data.
What are the main challenges in flattening data?
Flattening data can be complex due to issues like data redundancy, loss of context, and handling nested structures.
Can you explain what data redundancy means in this context?
Data redundancy refers to the unnecessary duplication of data, which can lead to increased storage costs and inconsistencies.
How does loss of context affect data analysis?
Loss of context can lead to misinterpretation of data, making it difficult to derive accurate insights or conclusions.
What are some best practices to avoid these challenges?
Best practices include using proper data modeling techniques, maintaining clear documentation, and leveraging ETL tools.
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