Databricks Azure Data Factory UC4 Job delta optimization and diverse strategies for ETL process and Data sourcing work flow process strategies
To load data from S3 files into Delta tables, transform them, and create an audit Delta table version using PySpark, we can follow these steps: Steps: 1. Set Up Configurations: Connect PySpark with AWS S3. 2. Load Data from S3: Read the files into a DataFrame. 3. Transform Data: Apply minimal transformations based on your requirements. 4. Write Data to Delta Tables: Write the transformed data to a Delta table. 5. Create an Audit Delta Table: Add metadata (e.g., version, timestamps, operations) for auditing purposes. Prerequisites: PySpark is configured with Delta Lake. AWS credentials are accessible (either in the environment or through a credentials file). Delta Lake libraries are included in the PySpark environment. --- Example Code: from pyspark.sql import SparkSession from pyspark.sql.functions import current_timestamp, lit # Initialize Spark session with Delta Lake support spark = SparkSession.builder \ .appName("S3 to Delta with Audit") \ .config(&qu