What are common mistakes in data architecture?
An example of an AI chatbot conversation about data architecture mistakes.
What are common mistakes in data architecture?
Common mistakes include poor data modeling, lack of scalability, and ignoring data governance.
Can you explain why poor data modeling is a mistake?
Poor data modeling can lead to inefficient queries, data redundancy, and difficulties in data integration.
What about scalability issues?
Scalability issues arise when the architecture cannot handle increased data loads, leading to performance bottlenecks.
How can I avoid these mistakes?
To avoid these mistakes, focus on proper planning, use best practices in data modeling, and regularly review your architecture.
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