How do you measure the success of your data warehouse optimization?

Every query should be efficient and reliable - and this should be a practice since the beginning of every project. The volume of data will increase over time and the possibility to add more complexity in an existing warehouse should be also considered as part of any long term project. Queries/ processes taking too many hours should be avoided, and every query trace should be analyzed to ensure the best approach/solution - this will help in the performance but also to avoid data issues in the future.

Implement query optimization techniques such as indexing, partitioning, and denormalization to improve query performance. Design an efficient data model and schema that aligns with your query patterns and use cases.

There are multiple aspects of measuring Optimization success . General thinking is that reducing query response time is only metric . But that’s only partially true . You should aim not only for reducing query run times but optimizing overall throughput and that comes from efficient usage of resources like CPU , memory and I/O . Also query response time reduction should be done based on previously established Performance benchmarks . These performance benchmarks are mainly established based on use cases , their business SLAs and thersholds. This will ensure we spend right amount effort and produce desirable results . Contrary to common perception , performance tuning is not only about SQL , but it can very well be about Process optimization

The amount and type of the user feedback you can receive, it can use to define a measure to success in your data warehouse. In my opinion, there are 3 type of feedback oriented of the data warehouse: > The performance: how fast you get the data, lifetime of the available dataset, etc, > The improvement: add new entities or fields, add new storage to the infrastructure, etc > The usability: how to use the entities, what meaning the entities, what business processes can support based in subject, etc. If you get more positive usability feedback all time than other options; your data warehouse is succesfull from the user experience perspective.

Use user feedback as a continuous source of insight for iterative improvement. Highlight positive feedback and success stories from users to celebrate the impact of optimization efforts.

Establish thresholds or targets for each metric based on your data warehousing requirements and performance goals. Encourage collaboration and knowledge sharing among data engineering, operations, and analytics teams to exchange best practices, lessons learned, and innovative solutions for optimizing resource utilization and driving cost savings.

Implement data cleansing techniques such as standardization, validation, and deduplication to improve data quality and integrity.

Implement elasticity and auto-scaling capabilities to automatically adjust resources based on workload demand. Set up alerts for thresholds such as CPU utilization, memory usage, etc.

You do optimizations always for a reason. Once the reason is gone, you know you were successful ?? So measuring the success should be straightforward - in theory.