DP-203 · Question #165
You are implementing a batch dataset in the Parquet format. Data files will be produced be using Azure Data Factory and stored in Azure Data Lake Storage Gen2. The files will be consumed by an Azure…
The correct answer is A. Use Snappy compression for files. The Snappy compression algorithm provides a good balance between compression ratio and decompression speed, making it an efficient choice for Parquet files. By compressing the files, you can significantly reduce the storage costs associated with storing large volumes of data…
Question
You are implementing a batch dataset in the Parquet format. Data files will be produced be using Azure Data Factory and stored in Azure Data Lake Storage Gen2. The files will be consumed by an Azure Synapse Analytics serverless SQL pool. You need to minimize storage costs for the solution. What should you do?
Options
- AUse Snappy compression for files.
- BUse to query the Parquet files.
- CCreate an external table that contains a subset of columns from the Parquet files.
- DStore all data as string in the Parquet files.
How the community answered
(48 responses)- A83% (40)
- B2% (1)
- C10% (5)
- D4% (2)
Explanation
The Snappy compression algorithm provides a good balance between compression ratio and decompression speed, making it an efficient choice for Parquet files. By compressing the files, you can significantly reduce the storage costs associated with storing large volumes of data. Additionally, Snappy compression is supported by both Azure Data Lake Storage Gen2 and Azure Synapse Analytics, making it an ideal solution for this scenario.
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