What is the difference between a data lake and a data warehouse?
An example of an AI chatbot conversation about data lakes and data warehouses.
What is the difference between a data lake and a data warehouse?
A data lake is a storage repository that holds vast amounts of raw data in its native format, while a data warehouse stores structured data that has been processed for analysis.
Can you explain the use cases for each?
Sure! Data lakes are great for big data analytics and machine learning, while data warehouses are ideal for business intelligence and reporting.
Which one is more cost-effective?
Data lakes are generally more cost-effective for storing large volumes of data, as they use cheaper storage solutions. However, data warehouses can provide faster query performance for structured data.
Are there any limitations to using a data lake?
Yes, data lakes can become disorganized and harder to manage without proper governance, leading to issues like data quality and security.
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