1. Object
   1. Cloud Storage
      1. Good for:
         1. Binary or object data (Images, media serving, backups)
      2. Use cases
         1. Website content
         2. Storing data for archiving and disaster recovery
         3. Distributing large data objects to users via direct download
      3. Key features
         1. Scalable to exabytes
         2. Very high availability across all storage classes
      4. Location types
         1. Multi-region
            1. Is a large geographic area, such as the United States, that contains two or more geographics places
            2. Objects stored here are geo-redundant
         2. Dual-region
            1. Is a specific pair of regions, such as Finland and the Netherlands
            2. Objects stored here are geo-redundant
         3. Region
            1. Is a specific geographic place, such as London
      5. Storage classes
         1. Standard
            1. Is best for data that is frequently accessed ("hot" data) and/or stored for only brief periods of time
            2. This is the most expensive storage class
            3. It has not minumum storage duration
            4. SLA -> 99.95% (multi/dual), 99.90% (region)
         2. Nearline
            1. Is a low-cost, highly durable storage service for storing infrequently accessed data like data backup, long-tail multimedia content, and data archiving
            2. It has 30-day minimum storage duration
            3. SLA -> 99.90% (multi/dual), 99.00% (region)
         3. Coldline
            1. Is a very-low-cost, highly durable storage service for storing infrequently accessed data
            2. It has a 90-day minimum storage duration
            3. SLA -> 99.90% (nulti/dual), 99.00% (region)
         4. Archive
            1. Is the lowest-cost, highly durable storage service for data archiving, online backup, and disaster recovery.
            2. It has a 365-day minimum storage duration
            3. SLA -> None
2. File
   1. Filestore
      1. Is a managed file storage service for applications that require a file system interface and a shared file system for data
      2. Good for:
         1. Network Attached Storage - NAS (Latency sensitive workloads)
      3. Use cases
         1. Application migration
         2. Media rendering
         3. Electronic Design Automation (EDA)
         4. Data analytics
         5. Genomics processing
         6. Web content management
3. Relational
   1. Cloud SQL
      1. Is a fully managed service of either MySQL, PostgreSQL, or Microsoft SQL Server databases
      2. Good for:
         1. Web frameworks (CMS, eCommerce)
      3. Performance
         1. 64 TB of storage
         2. 60,000 IOPS
         3. 624 GB of RAM per instance
         4. Scale out with read replicas
   2. Cloud Spanner
      1. Combines the benefits of relational database structure with non-relational horizontal scale
      2. Key features
         1. Scale to petabytes
         2. Strong consistency
         3. High availability
         4. Horizontal scalability
         5. Used for financial and inventory applications
      3. Good for:
         1. RDBMS + scale, HA, HTAP (User metadata, Ad/Fin/Mar Tech
4. Non-relational
   1. Firestore
      1. Is a fast, fully managed, serverless, cloud native, NoSQL, document database that simplifies storing, syncing and querying data for mobile web and IoT apps at global scale
      2. Good for:
         1. Hierarchical, mobile, web (User profiles, game state)
      3. Key features
         1. Live synchronization and offline support
         2. Security features
         3. ACID transactions
         4. Multi-region replication
   2. Cloud Bigtable
      1. Is a fully managed NoSQL wide-column database
      2. Key features
         1. Petabyte-scale
         2. Consistent sub-10ms latency
         3. Seamless scalability for throughput
         4. Learns and adjusts to access patterns
         5. Storage engine for ML applications
         6. Easy integration with open source big data tools (Hadoop, Cloud Dataflow, Cloud Dataproc)
      3. Good for:
         1. Heavy read + write, events (AdTech, Fintech, IoT)
         2. Best option for streaming IoT data
         3. Ingesting the data
         4. Real-time analytics and high-performance
5. Warehouse
   1. BigQuery
      1. Fully-managed, highly-scalable, and cost-effective data warehouse designed for large-scale data analytics
      2. Is the default storage for tabular data
      3. Optimized for large-scale, ad-hoc SQL-based analysis and reporting
      4. Works well in case where the data does not change often
      5. Good for:
         1. Enterprise data warehouse (Analytics, dashboards)
         2. Long-term storage and analytics
6. Memory
   1. Memorystore
      1. Is a fully managed Redis service
      2. Key features
         1. In-memory data store service
         2. Focus on building great apps
         3. High availability, failover, patching, and monitoring
         4. Sub-millisecond latency
         5. Instances up to 300 GB
         6. Network throughput of 12 Gbps
         7. Easy Lift-and-Shift