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Learn how to save your parquet files with specific names in Databricks when writing to Azure Data Lake, ensuring compatibility with Azure Data Factory copy activities.
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How to Specify a Parquet File Name When Saving in Databricks to Azure Data Lake

When working with Databricks and Azure Data Lake, you might encounter a situation where you want to save your parquet files with a specific name instead of the automatically generated names. This particular need often arises when you're planning to utilize these files in Azure Data Factory for copy activities. In this guide, we will address this issue and provide a solution to help you achieve your goal.

The Problem

You may have noticed that when you try to save DataFrames in Databricks as parquet files, the system creates a folder named after the dataset (e.g., Covid19_Cases) and generates parquet files within that folder with random names. This automatic behavior can complicate data handling in Azure Data Factory, as it requires specific file names for efficient processing.

For example, when executing the following command:

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What results is a folder with unconventionally named parquet files:

Covid19_Cases/part-00000-xxxx.snappy.parquet

Covid19_Cases/part-00001-xxxx.snappy.parquet

To utilize specific files in Data Factory, you need consistent naming rather than the system-generated names.

The Solution

Understanding Spark's Behavior

First, it's essential to understand that Spark processes data in a distributed mode. This means that when a DataFrame is saved, it gets divided and written in chunks across multiple files for efficiency. As a result, multiple files are created rather than a single one. However, you can use a workaround to ensure you have a single parquet file with a specific name.

Step-by-Step Guide to Naming Your Parquet File

Here’s an approach you can follow to save DataFrames in Databricks to Azure Data Lake with a designated file name:

Set the Save Locations:
Define your paths clearly to work with folders and file names.

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Write the DataFrame:
Use the repartition method to ensure that the DataFrame is consolidated into a single partition. This helps to create only one output file.

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Copy the File:
Next, list the contents of the temporary parquet location and copy the newly created file to your desired location.

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Clean Up:
Finally, remove the temporary parquet folder as it is no longer needed.

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Final Thoughts

By following the steps outlined above, you can effectively save your DataFrames as parquet files in Databricks with specific file names in Azure Data Lake. This ensures that your files are compatible with Azure Data Factory operations, allowing for smooth data copying and manipulation.

Implementing this process may seem a bit tedious at first, but once you've set it up, it will streamline your workflow significantly. Saving data with specific file names can greatly enhance your data management strategies in Azure Data Lake.

If you have any questions or need further clarification on the approach discussed, feel free to leave a comment below!