SQL Practice Problems - Day 12
Focus: Delta Lake Operations (Databricks Specific)

1 - Merge new products from a source table into the target Delta table.
Input Tables: products_delta (target), products_staging (source)
Output: Updated products_delta table

2 - Upsert sales transactions from a daily batch into the main sales table.
Input Tables: sales_delta (target), sales_daily (source)
Output: Updated sales_delta table

3 - Handle schema evolution by adding new columns during merge operations.
Input Tables: customers_delta (target), customers_enriched (source)
Output: customers_delta with new columns

4 - Implement SCD Type 1 for customer dimension updates.
Input Tables: dim_customers_delta (target), stg_customers (source)
Output: Updated dim_customers_delta table

5 - Merge inventory updates while handling quantity adjustments.
Input Tables: inventory_delta (target), inventory_updates (source)
Output: Updated inventory_delta table

6 - Use MERGE with conditional updates based on business rules.
Input Tables: pricing_delta (target), pricing_updates (source)
Output: Updated pricing_delta table

7 - Implement incremental load with version tracking for audit purposes.
Input Tables: sales_delta (target), sales_incremental (source)
Output: Updated sales_delta with version metadata

8 - Handle soft deletes using MERGE operations with is_deleted flag.
Input Tables: products_delta (target), products_deletes (source)
Output: products_delta with soft-deleted records

9 - Merge data from multiple source tables into a single Delta table.
Input Tables: consolidated_sales_delta (target), online_sales, store_sales
Output: Updated consolidated_sales_delta table

10 - Use MERGE with complex join conditions and multiple match clauses.
Input Tables: customer_orders_delta (target), order_updates (source)
Output: Updated customer_orders_delta table

11 - Implement data quality checks during merge operations using constraints.
Input Tables: products_delta (target), products_updates (source)
Output: Validated products_delta updates

12 - Handle late-arriving facts using MERGE with historical data.
Input Tables: sales_delta (target), late_arriving_sales (source)
Output: Updated sales_delta with late data

13 - Use MERGE with time travel to correct historical data issues.
Input Tables: sales_delta (target), data_corrections (source)
Output: Corrected sales_delta table

14 - Implement CDC (Change Data Capture) pattern using MERGE operations.
Input Tables: cdc_target_delta (target), cdc_stream (source)
Output: Synchronized cdc_target_delta table

15 - Optimize Delta table performance during merge operations with Z-order.
Input Tables: large_sales_delta (target), daily_sales (source)
Output: Optimized large_sales_delta table