How can you optimize database performance after migration or integration?

Optimizing database performance post-migration or integration requires meticulous analysis. Start by reviewing queries, indexing, and data distribution. Adjust configurations, refine queries, and utilize caching strategies. Regular monitoring and tuning are crucial. Collaboration with database administrators and developers ensures a holistic approach, fostering continuous improvement.

Selecting the ideal model for your application and data is critical. The options? Relational, non-relational, and hybrid models. Each with its own advantages and disadvantages. What to consider? Scalability, flexibility, consistency, and complexity. Examples: Structured data with complex transactions: Relational model. Unstructured or semi-structured data: Non-relational model. Choosing wisely will give you optimal performance in the long run.

A database's success hinges on its performance. To optimize it, we must: * Choose the right data model: Relational, non-relational, or hybrid, based on needs. * Index the data: Create shortcuts for fast and efficient access. * Optimize the schema: Eliminate redundancy, improve access, and simplify queries. * Select hardware and software: CPU, RAM, storage, operating system, database engine, and management tools. * Monitor and optimize: Adjust configuration, implement caching, and develop efficient queries. By following these keys, we can transform our database into a success engine for the enterprise.

Caching stores frequently accessed data in a fast, temporary layer, like memory or disk. - Benefits: * Reduces database load * Improves response time * Saves bandwidth and resources - Drawbacks: * Cache invalidation management * Synchronization and consistency - Choose wisely: * Match strategy to application and data Consider data size, volatility, and access frequency - Examples: * Local vs. distributed caching * Read-through vs. write-through caching * Time-based vs. event-based caching Caching can significantly improve performance, but it's not a magic bullet.

Optimizing our database performance post-migration involves fine-tuning queries for efficiency. This means scrutinizing and enhancing query syntax, logic, and execution plans to minimize data retrieval or modification costs and time. By refining queries, we can sidestep unnecessary joins, subqueries, functions, or conditions, employ optimal operators and filters, and leverage query hints and parameters. To achieve this, we should utilize tools like explanation plans, query analyzers, profilers, and benchmark tests. These resources help us identify and rectify performance bottlenecks and errors, ensuring our database operates at its peak.

1. Identify the problem queries- SQL Server Profiler, Extended Events to capture queries that cause performance issues. Extract the Query Execution plans from SSMS or any Data Studio to identify expensive operations - table scans, index scans, nested loops, etc. 2. Optimize Query Structure - JOINs instead of subqueries, and EXISTS or IN instead of DISTINCT or GROUP BY, Avoid functions or expressions in WHERE clause, and minimize using wild characters at the beginning of LIKE. 3. Experiment with query hints like OPTION (RECOMPILE) or OPTION (FORCE ORDER) to force specific execution plans or to recompile dynamic queries. 4. Query & Plan Caching - use store procedures or parameterized queries.