How can you maximize query performance in your data warehouse?

Data Modeling and Indexing: Design your data warehouse schema and indexes with query performance in mind. Utilize star or snowflake schema designs for dimensional modeling, and create appropriate indexes on columns frequently used in queries, especially those involved in joins and predicates. Partitioning and Clustering: Partition large tables into smaller, manageable chunks based on a specific criterion such as date ranges or key ranges. Query Optimization: Optimize SQL queries by restructuring them to minimize the number of operations, reduce unnecessary joins, and leverage query hints or directives to influence the query execution plan.

Here are some steps that might help in maximizing the performance. 1) Have a clear-cut clarity on what schema would be helpful, whether it's a start schema or a snow flake schema. 2) Ensure proper indexing is maintained. 3) Take care of joins. Minimize complex joins or look for the optimal way of using joins and sub-queries. 4) Limit the no. of indexes; delete the indexes if the columns having those are not much used in query browsing. 5) Look for normalization process when ever needed.

Optimize SQL queries to leverage the schema design effectively and minimize query execution time. Use query optimization techniques such as indexing hints, query rewriting, and query plan analysis to improve query performance and efficiency.

For performance optimization it is key to set Performance benchmarks . The main drivers are Business use cases , their SLAs and thersholds . Otherwise Performance improvement will be very subjective. SLAs can be also purely based on infrastructure resources like CPU , I/O , storage and memory . For these bring in cost governance framework in place which will set various thersholds on consumption of these resources and monitor for breaches. Once the benchmarks are set , bring monitoring in place for end to end Data integration and reporting . If there is consistent pattern of SLA breaches . See if these breaches has any patterns with Resource consumption breaches . If they do , that is the area you need to investigate further .

Allocate resources such as CPU, memory, and storage capacity based on the requirements of individual queries and workloads. Explore automation tools and scripts to streamline workload management tasks and reduce manual intervention.

If the dataset is huge, then implementing partitioning would largely help; so that less data can be scanned in a particular phase and it would increase the query performance. Based on the requirement, have a clarity on table partitioning like whether it need to be date partitioning or range partitioning or conditional partitioning or hash partitioning.

Partitioned indexes can be aligned with partition boundaries, allowing for efficient index scans and range-based queries within individual partitions.

Try to minimise the amount of times you repeat a piece of models logic. I've seen far too many of the same CASE() states multiple times throughout the SQL. This not only has an effect on performance, but increases the risk of being missed if this logic needs to be modified or changed.

Minimize the use of subqueries or nested queries in SQL statements, as these can lead to inefficient query execution plans. Apply filters and predicates as early as possible in the query execution process to minimize the amount of data processed.

The caching could be act like layer to solve the high frequency access of the data in your data warehouse; but it depend how manage the cache to give a good performance: strategies like Write-Through, Read-Through or Cache-Aside are great options to accelerate your queries.

Preload frequently accessed data into the cache during system startup or idle periods to reduce the latency associated with cache misses.

Monitor key performance metrics such as query execution time, CPU usage, memory utilization, disk I/O, and network bandwidth. Track these metrics over time to identify trends, anomalies, or potential bottlenecks that may impact data warehouse performance.

We need to follow the AAA approach, i.e., Analyze, Actionize, then analyze again. for e.g. Before starting to work on maximizing the query performance, we need to spend some time monitoring the current data warehouse flow to understand the batch SLA and its impact on downstream applications. Then, we can choose one specific area where we can optimize. Once we have optimized that process, we can check the impact on other areas.