Amazon DocumentDB (with MongoDB compatibility) vs. Google Cloud Spanner

AWS Document DB (with MongoDB compatibility) is well suited when for all the workloads due to its huge feature offerings which will reduce our operational overhead and due to that we can focus more on our WorkLoad rather than optimising and fine tuning Databases. Its Offerings are Advanced Monitoring, DB cluster Upgrades, Migration Assistant, High Availability, Fault Tolerance, Data Durability, Security, Storage Auto Scaling, Backup Restore policies.AWS Document DB (with MongoDB compatibility) some of the features that are there in some other services like MongoDB Atlas that offers vast amount of features plus Supports Multi Cloud while Deploying Database clusters, Immediate support to latest Mongo DB versions, Mobile & Edge Sync like Atlas Edge Sync, Freedom to choose Database deployment in Any top Public Cloud, Having more then 100 plus Monitoring and Telemetry metrics for index and schema recommendations, More Compatibility with MongoDB queries.

Incentivized

Google Cloud Spanner is suited for limitless horizontal scaling while maintaining strong consistency which needs to support ACID. NoSQL databases work in scaling but no ACID support. RDBMS support ACID, but horizontal scaling is not as great. The API it provides result in some limitations to related areas of the code, such as connection pools or database linking framework. So high # of connection pools can vary.

Incentivized

• Amazon DocumentDB (with MongoDB compatibility) provides Auto scaling of cluster as a by default functionality through this we can focus on more on our applications end
• Through AWS Document DB without much operation overhead we can configure for Database's high availability, Durability, Backup Restores policies, Advanced Monitoring, Security Parameters.
• Also they can provide us a Guide for Database Migration from any Supported Mongo DB vendor to AWS Document DB.
• Via AWS Document DB query Logging ( Profiling ) we can fine tune our database queries and hence improving our END to END Customer Experience and Product Enhancements.

Incentivized

• Super high availability
• Scales automatically
• High standard SLA

Incentivized

• Give support for Latest Mongo DB versions available in market
• AWS Document DB is limited up to 32 shards per cluster and 2 shards per Document DB instance and all within single region
• Start supporting more numbers of Rich data types
• Should have access to MongoDB experts who throw light on Cutting edge mongoDB features and integration consulting.

Incentivized

• Support for Views
• Support for more databases (schemas).
• More index types that can be supported (Functional)
• Backups (ie table/data backup) if data is deleted or truncate by accident.

Incentivized

At that point, we were looking at something [that] can hold our relational database, [...] provide stable connection, and maintain high ACID transition. BigTable is for nonrelational database so it was out of our [sight] very quickly. BigQuery is a data warehouse that can hold huge amount of data but not ideal for transition. AWS RDS is [...] similar to Spanner but because most of our services are already on GCP, so we went with Spanner.

Incentivized

• Great Customer Experience as DB queries are fine tuned
• Less Operational Overhead to manage and take care of the Database
• Automatic applying of Small patches

Incentivized

• Backups specifically if transactional data is deleted. Restoring made us lose time.
• Sharding on Horizontal level was quick and easy. Deployment and increasing nodes is easy
• Large dataset handling.
• ACID compliance

Incentivized