DynamoDB is a fully managed NoSQL database service offered by Amazon Web Services (AWS). MongoDB Atlas, in contrast, is a managed database service designed to deploy, operate, and scale MongoDB, a popular NoSQL database engine.
While both are NoSQL databases, their underlying data models and access patterns differ. For a deeper dive into these distinctions, please refer to resources comparing DynamoDB vs. MongoDB.
Each service boasts distinct advantages:
DynamoDB:
- Zero-Maintenance Operations: Eliminates infrastructure management overhead.
- Ease of Use and Setup: Quick to learn and deploy.
- Simple Scalability: Scaling throughput capacity is straightforward and efficient.
MongoDB Atlas:
- Free Tier Availability: Offers a free instance ideal for development and testing environments.
- Up-to-Date MongoDB Versions: Benefits from immediate deployment of the latest MongoDB updates by the Atlas team.
- Optimized MongoDB Infrastructure: Built and managed by the creators of MongoDB, ensuring a highly optimized environment.
Integration Capabilities
Key Takeaway: DynamoDB, deeply integrated within the AWS ecosystem, offers seamless integration with other AWS services. MongoDB Atlas integration requires more custom development and maintenance.
DynamoDB’s strength lies in its native integration with the broader AWS ecosystem. It works seamlessly with services like AWS Lambda, enabling powerful serverless architectures.
Consider these integration examples with DynamoDB:
- Event-Driven Architecture: Trigger AWS Lambda functions automatically upon data changes (inserts, updates, deletes) in DynamoDB tables.
- Scalable APIs: Expose DynamoDB data through secure and scalable HTTP endpoints using API Gateway for external access.
- Big Data Analytics: Effortlessly load data from DynamoDB into Amazon Redshift for comprehensive big data analysis.
MongoDB Atlas, operating as an independent database system, lacks out-of-the-box integration with AWS services. Integrating Atlas with other AWS services necessitates custom development and implementation by your development team. This can increase development time and ongoing maintenance.
Scalability Considerations
Connection Management
Key Takeaway: DynamoDB leverages highly scalable HTTPs API endpoints. MongoDB, utilizing socket connections, can introduce potential bottlenecks in high-concurrency environments.
DynamoDB operations are connectionless. Applications interact with DynamoDB tables via HTTPs endpoints, a fundamentally scalable approach. This eliminates connection management overhead and inherent limitations associated with persistent connections.
MongoDB Atlas relies on TCP socket connections for database operations. There’s a limit on concurrent connections in MongoDB Atlas, dependent on the chosen server instance size1. This connection model can become a constraint, especially in serverless architectures or applications requiring high concurrency. For projects employing high-throughput compute services like AWS Lambda, MongoDB’s connection limits could become a bottleneck.
Throughput and Storage Scaling
Key Takeaway: DynamoDB offers superior, virtually limitless scalability. MongoDB Atlas is suitable for small to medium applications, but DynamoDB is better suited for projects expecting massive growth and high throughput demands.
MongoDB Atlas employs sharding2 to distribute data across multiple nodes to handle increased scalability needs. Sharding is a common technique for scaling out databases horizontally.
DynamoDB’s scalability is virtually unbounded. Its scaling model considers both data storage and input/output (I/O) demand. DynamoDB automatically adjusts the number of backend instances to accommodate increased read/write throughput demands, scaling transparently in the background.
A key distinction emerges: MongoDB Atlas exposes instance provisioning to the user, requiring manual scaling configurations. DynamoDB abstracts away infrastructure management entirely, offering auto-scaling capabilities.
Sharding in MongoDB can impose restrictions3 on certain database features. DynamoDB maintains feature parity regardless of scale. MongoDB’s sharding capacity and maximum data storage are determined by a specific formula4. In optimal configurations, MongoDB Atlas can store up to 32 Terabytes of data.
DynamoDB, on the other hand, supports virtually infinite data storage. Throughput is managed by provisioning I/O capacity5 or utilizing an on-demand capacity mode6, where DynamoDB dynamically optimizes I/O allocation based on real-time application demands.
In conclusion, DynamoDB’s scalability is far simpler to manage, demanding minimal developer intervention. MongoDB Atlas scalability, while capable, is less transparent and may not guarantee predictable throughput under extreme load.
Reliability and Data Durability
Replication and Distribution
Key Takeaway: Both services offer robust replication and distribution for high availability. MongoDB Atlas benefits from AWS infrastructure when deployed on AWS.
DynamoDB leverages AWS’s proven Multi-AZ7 and Multi-Region8 architectures, ensuring maximum reliability and fault tolerance. These architectures distribute data across multiple availability zones and regions for disaster recovery and high availability.
MongoDB Atlas employs replica sets9 to maintain data redundancy across multiple nodes, achieving high availability and data durability. When deployed on AWS, MongoDB Atlas benefits from AWS’s underlying infrastructure reliability.
Backup and Recovery
Key Takeaway: Backup options are comparable, meeting the backup and recovery needs of most cloud-based applications.
DynamoDB offers two backup strategies:
- On-Demand Backups: Create full table backups at any time, easily restorable with a single click.
- Point-in-Time Recovery (PITR): Maintains continuous, incremental backups, allowing restoration to any point in time within the retention period.
PITR provides protection against accidental data modifications. It eliminates the need for manual backup scheduling as it automatically tracks table versions. DynamoDB backups (creation and restoration) are remarkably fast, completing in seconds regardless of table size, without impacting performance. Backups are stored on highly durable, external storage.
MongoDB Atlas provides similar backup capabilities:
- Continuous Backups: Analogous to DynamoDB’s Point-in-Time Recovery, offering granular recovery.
- Cloud Provider Snapshots: Scheduled full database backups (daily), stored within your chosen cloud provider (AWS, GCP, Azure).
Portability and Vendor Lock-in
Key Takeaway: MongoDB Atlas, based on open-source MongoDB, offers greater portability. DynamoDB is proprietary, leading to vendor lock-in concerns.
MongoDB Atlas, built on the open-source MongoDB database, inherently provides higher portability. Atlas itself can be deployed across multiple cloud providers, offering flexibility.
However, some users have reported difficulties migrating data out10 of MongoDB Atlas, suggesting potential migration complexities despite the open-source nature of MongoDB. While not preventing migration, it may increase costs.
DynamoDB offers zero portability. It is exclusively an AWS service. Vendor lock-in is a significant consideration.
For projects prioritizing future cloud provider migration options, DynamoDB can lead to substantial migration costs and effort. No other database offers drop-in API compatibility with DynamoDB. Migrating away from DynamoDB necessitates significant application refactoring.
Security Features
Key Takeaway: Both services provide robust security suitable for most production cloud deployments. AWS offers enhanced security options for highly regulated industries.
DynamoDB provides straightforward encryption-at-rest, utilizing AWS-managed keys or customer-managed keys via AWS KMS (Key Management Service)11.
Encryption-in-transit is enforced through HTTPS REST API endpoints using TLS (Transport Layer Security) with top-tier encryption standards. For secure connections from on-premises systems, AWS offers Site-to-Site VPN connections12. DynamoDB offers granular access control through IAM (Identity and Access Management)15, enabling fine-grained permissions for various operations:
- Data-Level Operations: Querying, scanning, deleting, and updating items.
- Database-Level Operations: Creating, deleting tables, managing secondary indexes13, and enabling data streams14.
IAM roles and policies authenticate and authorize access to specific DynamoDB endpoints, ensuring robust security.
MongoDB Atlas employs IP address whitelisting for security, which can pose challenges for applications using ephemeral compute services like AWS Lambda (which lack fixed IPs unless within a VPC). Atlas clusters can be deployed within a VPC (Virtual Private Cloud16) for network isolation and enhanced security. MongoDB Atlas also supports TLS, LDAP17, and user authentication mechanisms for added security layers.
Footnotes
[1] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:1
[2] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:2
[3] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:3
[4] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:4
[5] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:5
[6] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:6
[7] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:7
[8] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:8
[9] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:9
[10] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:10
[11] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:11
[12] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:12
[13] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:13
[14] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:14
[15] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:15
[16] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:16
[17] https://dashbird.io/knowledge-base/dynamodb/dynamodb-vs-mongo-atlas/#fnref:17
