Data Warehouse 面接の質問と回答
質問 16. What is a star join in the context of Data Warehousing?
A star join is a type of join operation that involves connecting a fact table directly to one or more dimension tables. It is a key aspect of star schema design and helps simplify and speed up query processing.
Example:
Joining a sales fact table with 'Product' and 'Customer' dimension tables in a star schema.
質問 17. Explain the concept of slowly changing facts (SCF) in a Data Warehouse.
Slowly changing facts refer to the handling of changes in the measured values (facts) over time in a data warehouse. It involves managing updates or inserts to maintain historical accuracy in the facts.
Example:
Updating the sales quantity in a fact table to reflect changes over time due to corrections or adjustments.
質問 18. What is the difference between a data warehouse and a data mart?
While a data warehouse is a centralized repository that stores data from various sources for enterprise-wide analysis, a data mart is a subset of a data warehouse focused on a specific business unit or department.
Example:
A data warehouse may store company-wide sales data, while a data mart within it may focus specifically on regional sales.
質問 19. How does indexing impact the performance of a Data Warehouse?
Indexing involves creating data structures to quickly locate and retrieve rows from tables. In a data warehouse, proper indexing can significantly improve query performance by reducing the amount of data that needs to be scanned.
Example:
Creating indexes on columns frequently used in WHERE clauses to accelerate data retrieval in a data warehouse.
質問 20. What is the role of a star schema in enhancing query performance?
A star schema simplifies and speeds up query processing by connecting a central fact table to dimension tables. This design reduces the number of joins needed for queries, leading to faster and more efficient data retrieval.
Example:
Retrieving sales data by joining a fact table with 'Product' and 'Time' dimensions in a star schema.
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