A financial institution plans to migrate its on-premise financial reporting system, which relies on complex SQL queries and stored procedures, to a cloud-native architecture. Propose a high-level architectural design, including data storage, processing, and reporting layers, that addresses scalability, cost-efficiency, and data security for sensitive financial data.

MECE Framework: Data Storage, Processing, and Reporting. 1. Data Storage: Implement a cloud-native data warehouse (e.g., Snowflake, Google BigQuery) for structured financial data, leveraging columnar storage for query performance. Utilize object storage (e.g., S3, GCS) for unstructured data and backups. Encrypt all data at rest and in transit. 2. Data Processing: Employ serverless ETL/ELT tools (e.g., AWS Glue, Azure Data Factory) for data ingestion and transformation. Migrate complex SQL/stored procedures to cloud-native data pipeline services (e.g., Apache Airflow on Kubernetes, AWS Step Functions) for orchestration. Leverage managed services for scalability. 3. Reporting: Utilize cloud-native business intelligence tools (e.g., Power BI, Tableau Cloud, Looker) for interactive dashboards and ad-hoc reporting. Implement robust access controls (RBAC) and data masking for sensitive information. Ensure compliance with financial regulations via audit logging and monitoring.

Leveraging a MECE framework, a high-level architectural design for migrating an on-premise financial reporting system to the cloud involves distinct layers for Data Storage, Processing, and Reporting, prioritizing scalability, cost-efficiency, and security.

1. Data Storage: Implement a cloud-native data warehouse (e.g., Snowflake, Google BigQuery) for structured financial data, utilizing columnar storage for optimal query performance. For unstructured data and backups, leverage highly durable object storage (e.g., AWS S3, Azure Blob Storage). All data must be encrypted at rest and in transit using KMS-managed keys, with strict data residency controls.

2. Data Processing: Employ serverless ETL/ELT services (e.g., AWS Glue, Azure Data Factory) for data ingestion, transformation, and orchestration. Refactor complex on-premise SQL queries and stored procedures into cloud-native data pipeline services (e.g., Apache Airflow on Kubernetes, AWS Step Functions) to ensure scalability and maintainability. Utilize managed services to minimize operational overhead and optimize cost.

3. Reporting: Integrate with cloud-native Business Intelligence (BI) platforms (e.g., Power BI, Tableau Cloud, Looker) for interactive dashboards and ad-hoc analysis. Implement granular Role-Based Access Control (RBAC) and data masking techniques to protect sensitive financial information. Ensure comprehensive audit logging and monitoring for compliance with financial regulations (e.g., SOX, GDPR) and real-time threat detection.