One of your friends needs to replace the content of a table.
He is thinking of deleting the entire directory of the Delta table and creating a new table on the same path.
Is this a recommended solution?
Click on the arrows to vote for the correct answer
A. B.Correct Answer: B
The given solution is not recommended as: Deleting a directory is not effective.
A directory with very large files can consume hours or even days to delete.
You lose all content of the deleted files and it is quite hard to recover if you delete the wrong table.
The directory deletion is not atomic.
When you delete the table, a concurrent query reading the table might fail or see a partial table.
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The recommended solution for replacing the contents of a Delta table in Azure depends on the specifics of the use case and the requirements of the data engineering pipeline. However, deleting the entire directory of the Delta table and creating a new table on the same path is generally not recommended.
Here's why:
Data loss: Deleting the entire directory of a Delta table would result in data loss. All the data stored in that table would be permanently deleted, which may not be acceptable in most situations.
Operational overhead: Deleting and recreating a Delta table can be a time-consuming and resource-intensive process, especially if the table contains a large amount of data. It can also impact the performance of other jobs running in the same cluster.
Impact on downstream jobs: Deleting and recreating a Delta table can also impact any downstream jobs that rely on that table. This could result in delays or errors in the entire data engineering pipeline.
A better solution for replacing the contents of a Delta table is to use the Delta API or SQL commands to update the table with new data. This approach would preserve the existing schema and metadata of the table, avoid any data loss, and minimize the impact on the rest of the data engineering pipeline.
Overall, the recommended solution for replacing the contents of a Delta table on Azure should be determined based on the specific use case, the size of the data, and the requirements of the data engineering pipeline.