Choosing the Right Distributed Table Type for Large Fact Tables
Question
Cliff is a data analyst of Adatum Corp.
working on designing fact & dimension tables of a SQLdata warehouse on Star schema.
As an inventory application, there are several fact tables.
There are sizes of more than 10 GB.
The dimension tables are smaller in size & mostly, there's no joining key available on those dimension tables. Which kind of Azure SQL data-warehouse distributed table type should be used for large size fact tables?
Answers
Explanations
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A. B. C. D.Correct Answer: B.
When working with Azure SQL Data Warehouse and designing a star schema, it is important to consider the size of the fact tables and the distribution strategy to use. In this case, the fact tables are large (more than 10GB), and the dimension tables are smaller and mostly without joining keys.
The distribution strategy used for a fact table determines how the data is distributed across the compute nodes in the SQL Data Warehouse. This can affect query performance and the scalability of the system.
The available options for Azure SQL Data Warehouse distributed table types are:
A. Round-robin distributed table: This strategy distributes rows evenly across all compute nodes in a round-robin fashion, regardless of the values in any columns. This approach can be useful for tables without a clear distribution column or when you want to balance the workload across all nodes. However, it may result in poor performance if the queries require data from multiple nodes.
B. Hash distributed table: This strategy uses a hashing algorithm on a designated column to distribute rows across compute nodes. This ensures that rows with the same hash value are stored on the same compute node, which can improve query performance when joining data between tables. In this case, since there are no clear joining keys on the dimension tables, it may be challenging to use this approach.
C. Distribution column: This strategy designates a specific column in the table as the distribution key, and then data is distributed across compute nodes based on the values in that column. This approach can be useful when there is a clear distribution column, such as a date or location. However, since there are no clear joining keys available on the dimension tables, this may not be the best approach.
D. Replicated table: This strategy replicates the data across all compute nodes in the SQL Data Warehouse. This can be useful for small tables that are frequently accessed or used in queries, as it can improve query performance. However, it can also result in increased storage costs and may not be suitable for large fact tables.
Given the large size of the fact tables and the absence of joining keys on the dimension tables, the best approach would be to use a round-robin distributed table or a distribution column approach. The choice will depend on the specific requirements of the workload and the available columns for distribution.
