There is no simple answer to this and possible actions differ regarding expertise and effort required: 1. Check your SQL: often high potential, requires good expertise, is not always possible (e.g. if SQL is automatically generated by a reporting tool). If possible, I would try this first. 2. If no self-optimizing database is used: Optimize the database tables (partitioning, distribution, indexes, etc.). This can be time-consuming, as ETL processes may also have to be adapted. Medium-term: Migration to a self-optimizing database 3. If a scalable database is used in the cloud: Provision of more compute resources. Improvement is achieved in short term. But leads to high costs in the long term. Therefore, check steps 1 and 2 afterwards.

To achieve faster query results for clients, we can utilize techniques like data compression (Parquet, ORC), data aggregation, and distributed computing (Apache Drill, Presto). Cloud solutions, including serverless processing (AWS Lambda, Azure Functions) and data lakes (AWS S3), offer scalability and flexibility, while data virtualization tools like Denodo allow for real-time queries. Real-time performance monitoring and proof of concept implementations can help set expectations and optimize query performance effectively.

Consider alternative solutions. Importing and persisting data during off-peak hours might be more efficient than live querying. Upgrade! bump up the compute, improve joins, upskill to use better technology. Consider best practices like partitioning data, Implement a tiered storage strategy, utilizing hot storage for frequently accessed data and cold storage for infrequently accessed or archived data. While better solutions come at an extra cost, that is not always true.