Optimization techniques in SQL focus on improving the performance and efficiency of database queries and operations. The goal is to execute queries faster and consume fewer system resources. Here are some key optimization techniques in SQL:

1. Use Indexes: Indexes are data structures that improve the speed of data retrieval operations. They help the database engine quickly locate rows based on the indexed columns. Properly chosen and maintained indexes can significantly speed up query performance.
2. *Avoid SELECT : Instead of retrieving all columns from a table, specify only the columns you need in your SELECT statements. This reduces the amount of data transferred and processed, improving query speed.
3. Limit Data Retrieval: Use the LIMIT (or equivalent) clause to restrict the number of rows returned by a query. This is particularly useful for scenarios where you only need a subset of results.
4. Use Joins Wisely: Use the appropriate type of join (INNER, LEFT, RIGHT, etc.) based on your data requirements. Avoid joining large tables unnecessarily, as it can lead to performance issues.
5. Optimize Subqueries: Subqueries can impact performance. Try to rewrite them as joins or use common table expressions (CTEs) to improve readability and potentially performance.
6. Normalize and Denormalize: Proper normalization minimizes data redundancy and improves data integrity. However, denormalization (introducing controlled redundancy) can improve query performance in certain cases by reducing the need for complex joins.
7. Avoid Functions on Columns: Applying functions to columns in WHERE clauses can prevent the use of indexes and slow down query execution. Try to avoid using functions on indexed columns.
8. Use Stored Procedures: Stored procedures allow you to precompile SQL statements and reuse them, reducing the overhead of parsing and optimizing queries each time they’re executed.
9. Analyze Query Execution Plans: Most database systems provide query execution plans that show how the database will execute your query. Analyzing these plans can help identify performance bottlenecks.
10. Use UNION Instead of UNION ALL: If you need to combine results from multiple queries, consider using UNION to eliminate duplicates. However, if you’re sure there are no duplicates, UNION ALL can be faster.
11. Optimize LIKE Queries: Avoid leading wildcards (e.g., %text) in LIKE queries, as they can’t use indexes efficiently. Use full-text search features for more complex text-based searches.
12. Avoid Using DISTINCT: If possible, restructure your query to avoid using the DISTINCT keyword, as it can slow down query execution.
13. Use Connection Pooling: Connection pooling reduces the overhead of creating and closing connections to the database, improving the efficiency of handling multiple client connections.
14. Monitor and Analyze Performance: Regularly monitor query performance using tools or system views provided by the database system. Identify slow queries and optimize them.

Remember that optimization techniques can vary depending on the specific database system you’re using. Performance tuning is an ongoing process, and you should always test the impact of changes on a representative dataset before implementing them in a production environment.