HOTSPOT
You have an Azure Blob storage account that contains a folder. The folder contains 120,000 files. Each file contains 62 columns.
Each day, 1,500 new files are added to the folder.
You plan to incrementally load five data columns from each new file into an Azure Synapse Analytics workspace.
You need to minimize how long it takes to perform the incremental loads.
What should you use to store the files and format?
Answer:
Explanation:
Box 1 = timeslice partitioning in the foldersThis means that you should organize your files into folders based on a time attribute, such as year, month, day, or hour. For example, you can have a folder structure like /yyyy/mm/dd/file.csv. This way, you can easily identify and load only the new files that are added each day by using a time filter in your Azure Synapse pipeline12. Timeslice partitioning can also improve the performance of data loading and querying by reducing the number of files that need to be scanned
Box = 2 Apache Parquet This is because Parquet is a columnar file format that can efficiently store and compress data with many columns. Parquet files can also be partitioned by a time attribute, which can improve the performance of incremental loading and querying by reducing the number of files that need to be scanned123. Parquet files are supported by both dedicated SQL pool and serverless SQL pool in Azure Synapse Analytics2.
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