Prepare and transform data in the lakehouse

Here's what we covered:
✅ Data Import & Table Creation
Converted Parquet files into tables in a serverless environment.
Created fact and dimension tables using optimized writers for efficient storage and querying.
✅ Aggregate Tables
Showcased two methods to build aggregate tables:
1️⃣ PySpark: Ideal for flexibility and scalability.
2️⃣ Spark SQL: Cleaner, simpler, and familiar for SQL users.
✅ Key Highlights
Leveraged Lakehouse notebooks for seamless integration.
Utilized advanced features like partitioning, columnar storage (V-Order), and optimized writing for faster reads.
💡 Takeaway: Spark SQL offers a clean and intuitive approach for creating aggregate tables, making it my preferred choice