Automating Weekly Business Review Metrics on AWS with Amazon Redshift and Amazon QuickSight

This thesis describes the design of an end-to-end solution to automate Weekly Business Review (WBR) metrics and publish them as interactive dashboards in Amazon QuickSight. The WBR is the weekly meeting in which teams and the organization review operational KPIs against targets and focus on corrective actions. The project, conducted in collaboration with Amazon S.à r.l., Luxembourg, starts from a spreadsheet-based process, then it migrates to a cloud architecture which preserves historical data, decouples operational monitoring from the data warehouse, and provides stable dashboard performance for the weekly review. At the heart of the system is the Redshift Visibility Tool (RVT), which queries Amazon Redshift system tables and views (query history, errors, usage statistics) on a schedule and writes clean, time-stamped datasets to Amazon S3 in Parquet. These datasets are then registered in AWS Glue and queried with AWS Athena. Eventually, Amazon QuickSight consumes them either directly or via SPICE for low-latency and high-concurrency reads. The solution integrates with Sophon, which provides infrastructure-as-code for QuickSight, user synchronization, governed ingestion/validation, DataMesh monitoring and notifications, and a system catalog. The thesis addresses the requirements with KPI-to-component traceability, diagrams of the architecture and integrations, and the motivations for adopting decoupled storage and compute on S3, IAM and cross-account access, and clear orchestration/monitoring. The design emphasizes predictability (in cost and performance) and operational robustness. Finally, the thesis sets out the main design trade-offs and gives a simple step-by-step guide to a representative KPI, tracing the data from the original source to the final chart. In practice, this replaces manual WBR spreadsheets with an automated cloud workflow that scales across the organization.