A financial institution needs to store and analyze sensitive customer data while adhering to strict regulatory compliance requirements. What Google Cloud storage and data analytics services should they use and what data protection measures should be applied?

For a financial institution storing and analyzing sensitive customer data under strict regulatory compliance, Google Cloud Platform (GCP) offers several services with robust security and compliance features. The key is to select the right storage and analytics services and to apply appropriate data protection measures. Here's a detailed breakdown: 1. Storage Services: Cloud Storage: For storing unstructured data such as documents, images, and large data files, Cloud Storage is a suitable choice. It offers various storage classes to optimize cost and access frequency (e.g., Standard, Nearline, Coldline, Archive). In this use case, Standard is more suitable for the frequently used data and nearline/coldline can be used for data that is infrequently accessed. Cloud SQL: For relational data, use Cloud SQL. It is fully managed and supports various database engines (e.g., PostgreSQL, MySQL, SQL Server). It provides built-in security features, automated backups, and high availability. Cloud SQL provides support for relational databases which is required for transaction based data that the institution must track and manage. Cloud Spanner: For globally distributed transactional data, consider Cloud Spanner. It provides strong consistency and scalability, and it's suitable for financial applications that require real time transactions across the globe. It is the best option for financial data that needs to be highly consistent, scalable and available. Bigtable: For large-scale, non-relational data, or when needing extremely low latency reads and writes consider Bigtable. It can be used for storing time series data, IoT data, or other unstructured data where scalability is critical. This is suitable for high throughput needs and would be relevant for the financial data, where large amounts of time series data might be present. Example: The financial institution can store cust....