How read a target table column data types and cast the same columns of the source table in azure databricks using pyspark

How to copy a delta table with dynamically casting all the columns to the data type of the target delta table columns in azure databricks using pyspark In this blog post, I will show you how to copy a delta table with dynamically casting all the columns to the data type of the target delta table columns in azure databricks using pyspark. This is useful when you want to migrate data from one delta table to another without losing any information or causing any errors due to incompatible data types. The main steps are: 1. Read the source and target delta tables using spark.read.format(“delta”).load() 2. Get the schema of the target delta table using target_table.schema 3. Loop through the columns of the source delta table and cast them to the corresponding data type of the target delta table using source_table.withColumn(col, col.cast(target_schema[col].dataType)) 4. Write the casted source delta table to the target delta table location using spark.write.format(“delta”).mode(“overwrite”).save() <a href="https://dezimaldata.medium.com/how-read-a-target-table-column-data-types-and-cast-the-same-columns-of-the-source-table-in-azure-62861d63b708">Read More</a>