A large energy company is using Amazon QuickSight to build dashboards and report the historical usage data of its customers. This data is hosted in Amazon Redshift. The reports need access to all…

The Amazon QuickSight SPICE (Super-fast, Parallel, In-memory Calculation Engine) engine is designed to accelerate interactive dashboard performance by caching data in its high-performance, in-memory columnar store. By importing the dataset into SPICE, QuickSight serves queries from its optimized cache, drastically reducing latency and the load on Amazon Redshift, thereby speeding up report response times with minimal implementation effort.

Using AWS Glue to create an Apache Spark job for pre-joining data would involve building a new ETL pipeline, which requires significant implementation effort and does not directly optimize QuickSight's interactive dashboard performance; it's an upstream data preparation step.

Creating a materialized view in Amazon Redshift can improve query performance by pre-computing results on the Redshift side. While beneficial for Redshift queries, QuickSight would still need to query Redshift for every interaction unless SPICE is used, and it involves more effort than simply configuring SPICE for the existing QuickSight dataset.

Creating a stored procedure in Amazon Redshift encapsulates SQL logic but does not inherently improve query performance by pre-computing or caching results. It would still execute complex joins and aggregations on demand, offering little improvement to the core issue of slow query execution over billions of records.