As a data engineer, you are assigned the task of choosing an ideal distribution column that will balance the parallel processing for your new distributed data table in the Azure Synapse Analytics dedicated pool.
Which of the following should be considered for the selection of this? (Multiple choice)
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A. B. C. D. E. F.Correct Answers: A, B and C
To balance the parallel processing, the selection of the distribution column is very important.
Otherwise, there are chances of data skew and processing.
This will affect the parallel query performances considerably.
The following are the three major considerations.
Has many unique values.
Duplicate values may be present in some columns.
Distribution will have entire rows with the same value mapped to it.
In fact, some columns will have more than one unique value, while some of them may not even have a single value.
Does not have NULLs or has only a few NULLs.
More number of nulls means more skew and thus decreases the performance of parallel processing.
Is not a date column.
In this case, all the data on a date will be in a single distribution.
Options A, B, C are correct: They are considerations that should be followed while selecting a distribution column.
Options D, E, F are incorrect: They are just opposite of what the real considerations are.
To know more, please refer to the docs below:
When creating a distributed data table in Azure Synapse Analytics dedicated pool, choosing the right distribution column is crucial to balance parallel processing. The following factors should be considered when selecting a distribution column:
A. Has many unique values A distribution column with many unique values is a good candidate for distribution. A high number of unique values ensures that data is distributed evenly across all nodes, which can improve query performance. For example, a unique customer ID column is a good choice for distribution.
B. Does not have NULLs, or has only a few NULLs If a distribution column has many NULL values, it can result in data skew and uneven data distribution across nodes. Therefore, it's recommended to select a column that either does not have NULL values or has only a few of them.
C. Is not a date column Date columns may not be a good choice for distribution as they may not have many unique values. This can result in data skew and uneven data distribution across nodes.
D. Is a date column While date columns may not be a good choice for distribution as they may not have many unique values, they can be a good candidate if the data is partitioned by date. Partitioning data by date ensures that data is evenly distributed across all nodes, which can improve query performance.
E. Have maximum number of nulls Columns with a high number of NULL values can result in data skew and uneven data distribution across nodes. Therefore, it's recommended to select a column that either does not have NULL values or has only a few of them.
F. Has least number of unique values Columns with the least number of unique values are not good candidates for distribution. They can result in data skew and uneven data distribution across nodes.
In summary, the ideal distribution column should have many unique values, does not have NULLs or has only a few of them, and may be a date column if the data is partitioned by date.