You are an Azure Data Engineer.
To accomplish your task, you need to design your table service solution to be read efficient.
Which of the following are the possible guidelines/ recommendations that you would follow to make your solution to be read efficient?
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answers: C and D
Here are some guidelines for designing table service solution to be read-efficient: Design for querying in read-heavy applications.
Specify both RowKey and PartitionKey in the queries.
Embrace storing duplicate copies for entities.
Embrace denormalizing your data.
Use compound key values.
Use query projection.
Option A is incorrect.
Read efficient solution advocates denormalizing the data, not normalizing as table storage is a cheap solution.
Option B is incorrect.
Not creating hot partitions is a guideline to design your Table service solution to be write-efficient.
Option C is correct.
Using compound key value is a recommended guideline for being the table service solution read -efficient.
Option D is correct.
Using query projection is a recommended guideline for being the table service solution read -efficient.
It enables reducing the amount of data that you transfer over the network with the help of queries selecting only the fields that are required.
Option E is incorrect.
The recommended guideline to be the table service solution read efficient is to specify both RowKey and PartitionKey in the queries.
To know more about guidelines for table design, please visit the below-given link:
As an Azure Data Engineer, if you want to design a table service solution to be read efficient, you can follow the following guidelines/recommendations:
A. Normalize the data as much as possible: Normalization is a database design technique that involves organizing data in a way that reduces redundancy and dependency. Normalization helps in reducing the data duplication and increases the efficiency of data retrieval, which makes it easier to read data.
B. Don't create hot partitions: A hot partition is a partition that receives more read or write operations than other partitions. When you create hot partitions, it causes performance issues because it puts a lot of load on a single partition, which can lead to throttling. To avoid hot partitions, you can use partition keys that distribute data evenly across partitions.
C. Use compound key values: Compound key values allow you to combine multiple values to create a unique key for each entity. This can help in organizing data in a way that makes it easier to read data. By combining multiple values, you can create a more granular key, which can help in reducing the number of entities returned by a query.
D. Use Query projection: Query projection is a technique that allows you to select only the columns that you need for a query. By selecting only the columns that are required, you can reduce the amount of data that needs to be transferred and improve the performance of the query.
E. Specify either Rowkey or Partitionkey (not both) in the queries: When querying data from a table, it is recommended to specify either the Rowkey or Partitionkey, but not both. This is because specifying both keys can cause unnecessary filtering and can increase the amount of data that needs to be transferred, which can reduce the performance of the query.
By following these guidelines, you can design a table service solution that is optimized for read efficiency.