To tackle performance issues after migrating large datasets, I’d compare pre- and post-migration query plans, update statistics, and use SQL advisors to optimize queries. If needed, I’d reintroduce the pre-migration plan or adjust indexing, partitioning, and configurations for better performance. Performance tuning is an iterative process!

1. Analyze Bottlenecks • Use Query Performance Insight and tools like SSMS or Azure Monitor to identify slow queries and resource bottlenecks. 2. Optimize Indexes • Utilize Automatic Tuning to add missing indexes and remove unused ones. • Manually create indexes for frequently queried columns if needed. 3. Optimize Queries • Review execution plans using Query Store to identify inefficiencies. • Avoid SELECT * and optimize joins and filters. 4. Leverage Scaling • Scale the database dynamically using the DTU model or vCore model to match performance needs. 5. Enable Features • Use In-Memory OLTP for high-throughput workloads. • Enable Azure SQL Automatic Performance Recommendations for ongoing tuning.

Optimize Data Partitioning and Indexing: Partition data based on query patterns and create indexes on frequently queried columns to improve performance. Batch Processing and Parallelism: Migrate data in batches and use parallelism to distribute the load for faster processing. Data Compression and Storage Optimization: Compress data for faster transfer and optimize storage configurations to improve performance. Monitor and Tune Performance: Monitor post-migration performance, optimize queries, and adjust configurations for efficiency.

Most performance issues are often due to poorly written queries. I start by identifying slow queries and, if necessary, rewriting them. If needed, I create appropriate indexes. I closely monitor resource consumption by the executed queries and implement proper configurations at the database and server level.