Implementing Data Partitioning Strategy for EmployeeID in Azure SQL Database
Question
You need to design an Azure SQL Database based on elastic pools.
You are planning to create a table Employee having the data about the employees.For each record, there is an EmployeeID.You need to implement a data partitioning strategy based on the values in EmployeeID.
You discuss with your friend and he suggests you to separate the data into Employee Departments via vertical partitioning.
Will it help you in achieving the goal?
Answers
Explanations
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A. B.Correct Answer: B
Using vertical partitioning in the given scenario won't help in achieving the goal.
Vertical partitioning is implemented for cross-database queries.
As we need to partition the data based on EmployeeID, here horizontal partition9 should be used.
Horizontal partitioning divides the data into partitions.
Each partition is known as a shard and holds a subset of the specific data.
Reference:
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No, vertical partitioning based on employee departments will not help achieve the goal of implementing a data partitioning strategy based on the values in EmployeeID.
Vertical partitioning involves splitting a table into multiple tables based on columns, rather than splitting it based on rows. In this case, if we partition the Employee table based on departments, we will have multiple tables, each containing data for a specific department. However, this approach does not address the goal of partitioning the data based on EmployeeID.
To implement data partitioning based on EmployeeID in Azure SQL Database, we can use horizontal partitioning, also known as sharding. This involves splitting the data horizontally into smaller subsets or shards based on a shard key, in this case, the EmployeeID. Each shard is then stored on a separate physical server, allowing for better scalability and performance.
We can use elastic pools in Azure SQL Database to manage the shards efficiently. An elastic pool is a collection of databases with a shared set of resources that can be scaled dynamically based on demand. By grouping the shards into elastic pools, we can manage the resources efficiently and ensure that each shard gets the required resources to perform optimally.
In conclusion, vertical partitioning based on employee departments will not help in achieving the goal of implementing a data partitioning strategy based on the values in EmployeeID. Instead, we should use horizontal partitioning or sharding to split the data into smaller subsets based on EmployeeID and manage them efficiently using elastic pools.
