Convert CSV to Parquet using pySpark in Azure Synapse Analytics

You've got a bunch of CSV files and you've heard of Parquet. How do you convert them for Azure Synapse Analytics? Patrick shows you how using pySpark.

Converting CSV files to Parquet format using pySpark in Azure Synapse Analytics is a great way to improve the performance of your data pipeline and reduce storage costs. Parquet is a column-oriented data storage format that is optimized for analytics. It is more efficient than CSV in terms of storage and processing time.

The process of converting CSV to Parquet using pySpark in Azure Synapse Analytics involves the following steps:

1. Create a connection to your Azure Synapse Analytics workspace.
2. Create a pySpark transformation job.
3. Read the CSV file into a DataFrame.
4. Write the DataFrame to Parquet format.
5. Monitor the job progress.

By leveraging the power of Azure Synapse Analytics and pySpark, you can easily convert your CSV files to Parquet format, improving the performance of your data pipeline and reducing storage costs.

pyspark DataFrame

[https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html]

pyspark.sql.DataFrameReader.load

[https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameReader.load.html]

pyspark.sql.DataFrameWriter

[https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrameWriter.html]

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