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You have an Azure Storage account that contains 100 GB of files. The files contain text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.
You plan to copy the data from the storage account to an enterprise data warehouse in Azure Synapse Analytics.
You need to prepare the files to ensure that the data copies quickly.
Solution: You convert the files to compressed delimited text files.
Does this meet the goal?
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A. B.A
All file formats have different performance characteristics. For the fastest load, use compressed delimited text files.
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-dataThe proposed solution of converting the files to compressed delimited text files could potentially meet the goal of copying the data quickly to an enterprise data warehouse in Azure Synapse Analytics. Here's why:
Compressed delimited text files: By compressing the files, the amount of data that needs to be transferred over the network will be reduced. This could lead to faster data transfers as less data needs to be moved across the network.
Text files: The text data format is a common and widely supported data format that can be read by most data integration tools. It is also easy to work with and process.
Delimited files: Delimited files, such as CSV (comma-separated values) files, are a common way of storing data in a tabular format. They are easy to generate and can be read by most data integration tools. By using delimited files, it is possible to break down the data into smaller, more manageable pieces, which could lead to faster data transfers.
However, there are some limitations to this solution that should be considered:
Compression overhead: While compressing the files can reduce the amount of data that needs to be transferred, there is an overhead associated with compressing and decompressing the files. This overhead can slow down the data transfer process.
Data format: While text files are easy to work with, they may not be the best choice for all types of data. For example, if the data contains complex structures, such as nested JSON or XML, it may be better to use a different data format, such as Parquet or ORC.
File size: The size of the files could impact the data transfer process. If the files are very large, they may need to be split into smaller files to facilitate faster transfers.
In conclusion, while converting the files to compressed delimited text files could potentially meet the goal of copying the data quickly, it is important to consider the limitations and trade-offs associated with this solution.