How to design an SQL-based reporting system that dynamically adjusts to handle varying data volumes and user queries without performance degradation?

Designing an SQL-based reporting system that dynamically adjusts to handle different data volumes and user queries can be quite a task, but with the right approach, you can set up a robust system. Here's your step-by-step guide on how to achieve that:

1. Understand Your Data: Start by knowing the types and volume of data you're dealing with. Is your data structured, unstructured, or a mix? How large are your tables? This will inform your database design, indexing, and query optimization strategies.

2. Choose the Right Database System: Pick a database system that can scale with your needs. For dynamic adjustment, cloud-based solutions like AWS RDS or Google Cloud SQL can be suitable as they offer easy scaling options.

3. Optimize Your Database Design: Use normalization to eliminate data redundancy and ensure the integrity of your data. However, be aware that overly-normalized databases can cause complex queries which may impact performance.

4. Use Indexing Strategically: Create indexes on the columns that are frequently used in search conditions to speed up query performance. Remember, too many indices can slow down insert and update operations, so balance is key.

5. Implement Caching: Use caching to store frequently accessed data in memory. This can reduce the load on the database server by serving repeated requests quickly without having to re-execute SQL queries.

6. Write Efficient Queries: Optimize your SQL queries to retrieve only necessary data. Utilize query performance tools that many SQL databases offer, to analyze and improve your queries.

7. Utilize Partitioning and Sharding: For very large databases, partitioning can help by breaking down a large table into smaller pieces, while sharding distributes data across multiple machines.

8. Monitor and Manage Workloads: Implement tools for monitoring queries and workloads. This helps you understand your system’s performance and identify bottlenecks or resource-intensive operations.

9. Automate Performance Tuning: Use features like SQL Server's Automatic Tuning or similar in other systems to automatically optimize performance by managing indexes and queries.

10. Test and Anticipate: Simulate different user behaviors and data volumes to see how your system performs under various scenarios. Use this to forecast future growth and adjust your system proactively.

11. Provide User Training: Educate users on best practices for querying the system. This can help reduce inefficient queries and the overall load on the database server.

12. Regularly Review and Update: Data patterns change over time, as does the volume. Regularly review the system performance and make adjustments to the indexing, partitioning, or scaling settings.

Remember, there's no one-size-fits-all solution when it comes to SQL-based reporting systems, and it's an iterative process that involves ongoing monitoring and tuning to match the changing loads and queries. Keep your system's design flexible and be prepared to adjust as needed.