SQL Practice Problems - Day 13
Focus: Redshift Performance Optimization

1 - Create a sales table distributed by customer_id and sorted by sale_date.
Input: Sales data structure
Output: Optimized sales table

2 - Bulk load product data from S3 into Redshift using COPY command.
Input: S3 bucket with product data
Output: Loaded products table

3 - Export customer data to S3 in Parquet format using UNLOAD command.
Input: Customers table
Output: S3 files in Parquet format

4 - Create a dimension table with DISTSTYLE ALL for small reference data.
Input: Product categories data
Output: Optimized categories table

5 - Design fact and dimension tables with appropriate distribution keys.
Input: Sales schema requirements
Output: Optimized table DDLs

6 - Load data from S3 with error handling and statistics update.
Input: S3 data with potential errors
Output: Clean loaded data with statistics

7 - Choose between EVEN and KEY distribution for large tables.
Input: Large sales data table
Output: Optimized distribution strategy

8 - Use Redshift Spectrum to query data directly from S3 without loading.
Input: S3 data files, external schema
Output: Query results from external data

9 - Create interleaved sort keys for multiple query patterns.
Input: Sales table with multiple access patterns
Output: Table with interleaved sort keys

10 - Implement data compression during COPY operations.
Input: Uncompressed S3 data
Output: Compressed table data

11 - Use AUTO distribution to let Redshift choose distribution style.
Input: New table with unknown usage patterns
Output: Table with AUTO distribution

12 - Optimize JOIN performance with distribution key alignment.
Input: Multiple large tables requiring joins
Output: Tables with aligned distribution keys

13 - Handle data loading with manifest files for multiple S3 objects.
Input: Multiple S3 files, manifest file
Output: Complete data load

14 - Create materialized views for frequently used aggregations.
Input: Large sales data, common queries
Output: Materialized view definition

15 - Implement workload management (WLM) for mixed workloads.
Input: Various query types and priorities
Output: WLM configuration