Amazon DocumentDB (with MongoDB compatibility) vs. Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)

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

If you need to execute ml algorithms, learning techniques, or mathematical calculations on large amounts of heterogeneous data, VMware Tanzu Data Services will be ideal. It will be really simple to set up, particularly if you choose AWS as your integrated cloud provider. However, if you're working with lower data amounts, such as gigabytes, it can be superfluous.

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

• Apache MADlib provides popular machine learning functionality.
• Allows you to query terabytes of data databases.
• Interoperability for AWS S3 is effortless.

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

• Running on Azure is a little more difficult.
• Synchronization with Kafka may be a little easier.

Incentivized

They were very helpful. We needed support for initial implementation.

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

• There was a noticeable reduction in system reliability.
• Saw a reduction in unsuccessful analytics operations.

Incentivized