Spark DataFrames in Databricks

All data operations in Databricks are performed through Spark DataFrames, which provide distributed data structures for large-scale analytics.

Related:

• PySpark

Typical Workflow:

1. Load external data:

   pdf = pd.read_csv(sheet_url)
   df = spark.createDataFrame(pdf)

2. Transform and preview:

   df.show(5)
   df.select("Type", "Status").distinct().show()

3. Write back to Delta:

   df.write.mode("overwrite").saveAsTable("example.databricks.databricks_test")

Advantages:

• Scalable beyond pandas limits.
• Integrates with SQL and machine learning tools.
• Native interoperability with Delta Lake.