In the second episode of the PySpark in Microsoft Fabric series, Austin Libal dives deeper into working with PySpark notebooks, focusing on Delta format integration and the use of SQL Magics. This session builds on the previous video, so viewers are encouraged to watch the first episode to set up their environment and Lakehouse.
Setting the Stage
- Begins by removing the existing holiday table from the Lakehouse to demonstrate a more efficient method of data handling using code.
- Introduces a new approach to load CSV data directly into a notebook cell using drag-and-drop functionality.
Creating a DataFrame from CSV
- Accesses the CSV file from the Lakehouse files folder.
- Reads the file into a DataFrame using PySpark with the header parameter set to
True.
- Displays the DataFrame to confirm successful data loading.
Writing to Delta Format
- Defines a variable
table_name with the value "holiday".
- Uses the
write function with overwrite mode to save the DataFrame in Delta format.
- Specifies the destination as the
tables folder in the Lakehouse using the variable.
- Executes the cell to write the data, effectively rebuilding the holiday table in Delta format.
Reconnecting to the Delta Table
- Demonstrates how to reconnect to the newly created Delta table using a new DataFrame.
- Confirms the data is accessible and correctly formatted.
Introducing SQL Magics
- Explains how to bridge PySpark and SQL using temporary views.
- Creates a temporary view from the DataFrame using
createOrReplaceTempView("holiday").
- Uses SQL Magic commands (e.g.,
%%sql) to query the data using familiar SQL syntax.
- Highlights the ability to switch cell languages via the notebook interface or magic commands.
Key Takeaways
- Using code to manage data in Microsoft Fabric is more flexible and scalable than relying solely on the GUI.
- Delta format ensures ACID compliance and better performance for Lakehouse tables.
- SQL Magics allow users to leverage their SQL skills within PySpark notebooks.
- Learning PySpark incrementally makes it approachable, even for those new to Python.
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