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 data compression?
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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 in 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 data compression.
For more details on columnstore indexes, please refer to the following document.
Yes, using clustered columnstore indexing will improve data compression in a traditional data warehouse storage with a snowflake schema that has row-oriented storage.
In a traditional data warehouse, data is typically stored in a row-oriented format where each row contains data for a single record. This can result in large amounts of duplicated data being stored, especially if the data has many columns and many records. This can lead to inefficient use of storage space and can also negatively impact query performance.
Clustered columnstore indexing is a feature in Microsoft SQL Server that allows data to be stored in a columnar format rather than a row-oriented format. This means that data is stored in columns rather than rows, and each column contains data for all records in a table. This can result in significant improvements in data compression because data is stored more efficiently, with less duplication.
When using clustered columnstore indexing, the data is divided into column segments that are compressed separately, allowing for more efficient use of storage space. Additionally, columnstore indexes support dictionary and value compression, which can further improve compression rates.
In summary, using clustered columnstore indexing can improve data compression in a traditional data warehouse storage with a snowflake schema that has row-oriented storage, leading to more efficient use of storage space and improved query performance.