What is the difference between data warehousing and data lakes?
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What is the difference between data warehousing and data lakes?
Data warehousing is a structured storage solution optimized for querying and reporting, while data lakes store raw data in its native format, allowing for more flexibility.
Which one is better for big data analytics?
It depends on your needs. Data lakes are better for unstructured data and exploratory analysis, while data warehouses excel in structured data and performance.
Can you give examples of when to use each?
Sure! Use a data warehouse for business intelligence reporting and a data lake for machine learning projects that require diverse data types.
What are the cost implications of each?
Data lakes can be more cost-effective for storing large volumes of data, while data warehouses may incur higher costs due to their structured nature and performance optimizations.
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