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Questions and Answers
Which of the following dimensions are part of the shipping fact table?
What advantage does partitioning provide in database management?
What is another term used for a fact constellation schema?
How does partitioning affect query performance in larger databases?
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What are the benefits of splitting larger databases into smaller pieces?
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Which measure is NOT included in the shipping fact table?
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What is a key reason for implementing partitioning strategies in data warehouses?
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Why is scanning a big table more time-consuming compared to smaller tables?
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What does a schema represent in a database context?
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Which of the following best describes a star schema?
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What is a key characteristic of a snowflake schema?
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In the context of a star schema, what type of data does the fact table primarily contain?
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What attribute is NOT typically found in the item dimension table of a snowflake schema?
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Which statement best describes the structure of a star schema's dimension tables?
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How does normalization in a snowflake schema benefit data management?
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What is a primary reason for using a star schema in data warehousing?
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How does partitioning affect the querying process for users?
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What is a primary benefit of processing complex calculations in memory due to accessing smaller datasets?
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What is the purpose of partition pruning in SQL queries?
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Which statement best describes the performance expectation when querying terabyte-sized tables in a data warehouse?
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What role does the optimizer play in partition pruning?
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Study Notes
Data Schema
- A logical description of the entire database
- Includes names and descriptions of records of all record types, including all associated data items and aggregates
Star Schema
- Each dimension in a star schema is represented with only one dimension table
- This dimension table contains the set of attributes
- Diagram showing the sales data with four dimensions: Time, Item, Branch, and Location
- Fact table at the center is linked to each dimension
- Fact table contains attributes, such as dollars sold and units sold
Snowflake Schema
- Some dimension tables in the snowflake schema are normalized
- Normalization splits data into additional tables
- Example: Item dimension table contains item_key, item_name, type, brand, and supplier_key
- Supplier-key links to supplier dimension table with attributes: supplier_key and supplier_type
Fact Constellation Schema
- Multiple fact tables
- Also known as galaxy schema
- Diagram shows sales and shipping fact tables
- Sales fact table is the same as the star schema
- Shipping fact table has five dimensions
- Item_key, time_key, shipper_key, from_location and to_location
- Shipping fact table contains two measures: dollars sold and units sold
- Dimension tables can be shared between fact tables
Partitioning Strategy
- Enhance performance
- Facilitate management of data
- Balance system requirements
- Optimize hardware performance
- Simplify data warehouse management by partitioning each fact table into multiple separate partitions
Bigger Databases
- Large database objects can be split into smaller pieces transparently
- Easier management of very large databases
Bigger Individual Tables: More Rows in Tables
- Takes longer to scan a big table than a small one
- Partitioned tables access only the partitions needed, effectively making them smaller
More Users Querying the System
- Users are likely to query on smaller data sets with partitioning
More Complex Queries
- Smaller data sets processed in memory, improving performance and reducing application I/O requirements
Partitioning for Performance in a Data Warehouse
- Analyses run against the database should return within a reasonable time, even if the queries access large amounts of data
Partitioning Pruning in a Data Warehouse
- Optimizer analyzes SQL statements to eliminate unneeded partitions, making querying faster and more efficient
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Description
This quiz covers various database schema designs including Data Schema, Star Schema, Snowflake Schema, and Fact Constellation Schema. Each schema is described in terms of structure, elements, and relationships, providing a comprehensive understanding of how they function in a database context.
