Scaling a data warehouse effectively starts with identifying bottlenecks—query optimization, indexing, and partitioning can make a significant difference. Evaluate your infrastructure; transitioning to a cloud-based or hybrid solution can provide on-demand scalability. Implement data lifecycle management to archive stale data and reduce load. Optimize ETL processes to handle increased data volumes efficiently. Leverage tools like materialized views or caching for frequent queries. Lastly, monitor performance regularly using analytics dashboards to stay proactive. #DataWarehousing #Scalability #PerformanceOptimization #CloudComputing #ETL

To improve performance in a data warehouse when it starts lagging, we can adopt the following methods: > Rewrite complex queries for efficiency, & limit the results with proper WHERE clauses > Denormalise data where necessary , to reduce needs for complex JOINS > Data Compression can also be implemented to reduce storage size and improve the READ operations Apart from these methods Indexing, Partitioning and Bucketing are the most common useful techniques to improve the performance

For large tables partitioning the data by date range may improve performance. Running update table/index statistics after bulk deletes and/or inserts.