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Answer: `df = spark.table("source_table"); df.write.saveAsTable("managed_table")`
To create a managed table in Databricks using PySpark, you should use the DataFrame API. First, load your source data into a DataFrame (e.g., df = spark.table("source_table")), then write it as a managed table using df.write.saveAsTable("managed_table"). This method ensures Databricks manages both the data and metadata, and no location is specified, so the table is fully managed by Databricks.
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Explain the process of creating a managed table in Databricks using PySpark. Include the necessary code snippet and explain each step of the process.
A
df = spark.table("source_table"); df.write.saveAsTable("managed_table")
B
spark.sql('CREATE MANAGED TABLE managed_table AS SELECT * FROM source_table');
C
spark.read.format('delta').load('/path/to/data').createOrReplaceTempView('managed_table');
D
spark.sql('CREATE TABLE managed_table USING DELTA LOCATION '/path/to/data';');
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