You have a traditional data warehouse storage with a snowflake schema with row-oriented storage that takes considerable time and low performance during queries.
You plan to use clustered columnstore indexing.
Will it improve query performance?
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A. B.Correct Answer: A
Most of the traditional data warehouses use row-oriented storage.
But columnstore indexes are used in modern data warehouses as the standard for storage and query in big data warehousing fact tables.
There are two advantages of using this while comparing with a traditional row-oriented Data warehouse.
10x performance in query performance.
10 x data compression.
Option A is correct: Using clustered columnstore will increase the query performance.
For more details on columnstore indexes, please refer to the following document.
The answer to this question is A. Yes, using clustered columnstore indexing can improve query performance for a data warehouse with a snowflake schema that currently has row-oriented storage.
A clustered columnstore index is a type of index in SQL Server that stores column data together in a compressed format, making it well-suited for data warehousing workloads. When a query is executed against a clustered columnstore index, the database engine can use batch processing techniques to scan and filter only the necessary columns, which can lead to significant performance improvements.
In contrast, traditional row-oriented storage stores data row by row, making it less efficient for analytical queries that typically involve scanning large numbers of columns. This can lead to slow query performance and increased storage requirements.
By implementing a clustered columnstore index, the data in the warehouse can be organized in a more efficient way, allowing for faster query performance and reduced storage requirements. However, it is important to note that implementing a clustered columnstore index may require changes to the database schema and may also require additional storage resources, so it should be carefully planned and tested before implementation.