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DynamoDB vs MongoDB

Amongst all NoSQL databases, DynamoDB and MongoDB stand out as two of the most popular choices in the market. As businesses embrace modern application development and data management, the decision to opt for DynamoDB vs MongoDB becomes critical.  Then, choice ideally comes down to your requirements and if you seek seamless scalability, real-time responsiveness, or flexibility in schema design.  

According to the Stack Overflow 2021 Developer Survey,  MongoDB, as the more trusted entity, had a market share of 27.7 per cent, while DynamoDB was steadily increasing in demand, capturing 7.3 per cent of the market. Being developed by Amazon and having extensive integration in Amazon Web Services has helped DynamoDB establish itself quickly. This blog will compare DynamoDB vs MongoDB in detail, evaluating their use cases, performance capabilities and strengths and weaknesses. 

Table of Contents 

1) Understanding DynamoDB and MongoDB 

2) DynamoDB vs MongoDB: A Comparative analysis 

         a) Data model 

         b) Scalability 

         c) Querying capabilities 

         d) Performance 

         e) Flexibility and schema 

         f) Indexing capabilities 

         g) Pricing  

         h) Data consistency and transactions 

3) Use cases 

4) Conclusion 

Understanding DynamoDB and MongoDB 

As businesses and applications generate vast amounts of data, the need for efficient and flexible databases has grown significantly. NoSQL databases, such as DynamoDB and MongoDB, address this demand by providing robust storage solutions. DynamoDB is a fully managed database service offered by Amazon Web Services (AWS), while MongoDB is an open-source document-oriented database. 

DynamoDB is a fully managed NoSQL database service provided by AWS. It is designed to offer seamless scalability, high availability, and low latency, making it a suitable choice for applications with rapidly changing workloads. Features of DynamoDB:

Features of DynamoDB

1) Efficient management: AWS handles all aspects of database management, including provisioning, setup, patching, and monitoring, allowing developers to focus solely on application development. 

2) Scalability: DynamoDB can automatically scale to accommodate varying workloads and storage needs. It employs partitioning to distribute data across multiple servers, ensuring even distribution of data and workload. 

3) Security: It provides robust security features, including encryption at rest and in transit, fine-grained access control, and integration with AWS Identity and Access Management (IAM). 

4) Low latency: DynamoDB's single-digit millisecond latency allows for fast read and write operations, allowing it to be suitable for real-time applications. 

MongoDB is a widely used open-source document-oriented NoSQL database. It stores data in BSON (Binary JSON) format, allowing for flexible and dynamic data structures. Features of MongoDB:

Features of MongoDB
1) Document-oriented: MongoDB stores data in flexible, JSON-like documents, enabling easy representation of hierarchical relationships and complex structures 

2) Scalability: It supports horizontal scaling through sharding, distributing data across multiple servers to handle large-scale applications 

3) Rich query language: MongoDB's query language allows for complex and expressive queries, facilitating powerful data retrieval. 

4) Secondary indexes: It supports secondary indexes, improving query performance by allowing data to be indexed on fields other than the primary key. 

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DynamoDB vs. MongoDB: A comparative analysis 

In this section, we will compare DynamoDB and MongoDB based on the following criteria: 





Deploy anywhere model 

Available on AWS only 

Data model 

MongoDB is a document store database  

DynamoDB is a key-value store database 


Powerful query language search for data by single keys, ranges, faceted search, JOINs, graph traversals, and geospatial queries 

Only supports key-value queries, and its primary key is limited to at most 2 attributes 


MongoDB offers robust indexing capabilities without extra cost.  

DynamoDB has limited indexing capabilities, and managing indexes can be complex. 

Monitoring and performance tuning 

Very transparent and shows 100+ metrics on database performance 

Black Box and has less than 35 metrics limit visibility into database behaviour 


With MongoDB Atlas, you can choose between different backup options, including on-demand, continuous, and snapshot backups.  

DynamoDB offers continuous backups, but they cannot be queried, and there is an additional charge to restore backups. 

Data integrity 

MongoDB offers strong consistency, meaning users always see the current data.  

DynamoDB is eventually consistent, meaning applications must handle stale data, and there is no built-in data validation. 


Consistent pricing model 

DynamoDB pricing is throughput-based and highly variable 


MongoDB Atlas offers Free, Dedicated, or Serverless instances 

DynamoDB only runs on Serverless instances. 


Data model 

The data model is a fundamental concept in database management, defining how data is structured, organised, and represented within a database. It acts as a blueprint that dictates how data will be stored, processed, and accessed. In the context of NoSQL databases like DynamoDB and MongoDB, the data model plays a crucial role in determining how data is managed. 

In DynamoDB, the data model revolves around key-value pairs and documents. Each item in the database is uniquely identified by a primary key, and additional attributes can be stored as key-value pairs. This model allows for quick retrieval based on primary key lookups and suits applications that require fast and predictable access to specific items. 

On the other hand, MongoDB adopts a JSON-like document data model. Data is stored as flexible and schema-less documents, allowing developers to work with evolving or dynamic data structures. This document-centric approach is well-suited for projects where data requirements may change over time and complex data structures are needed. 


Scalability is a crucial aspect of any modern application, as it directly impacts its ability to handle growing demands and increased user traffic. In the context of databases like DynamoDB and MongoDB, scalability refers to the system's capability to expand and accommodate larger datasets and workloads without compromising performance. 

In the case of DynamoDB, its seamless scalability is a standout feature. It allows developers to adjust capacity on the fly, automatically distributing data across multiple servers. As the application's demands fluctuate, DynamoDB can effortlessly scale up or down, ensuring that performance remains consistent even during peak times. This elasticity is particularly beneficial for businesses that experience sudden spikes in traffic or unpredictable workloads. 

On the other hand, MongoDB achieves scalability through its sharding mechanism. By horizontally distributing data across multiple shards or clusters, MongoDB can handle vast amounts of information and high-throughput operations. As the dataset grows, adding more servers to the cluster enables the database to handle increasing loads and maintain responsiveness. 

Querying capabilities 

Querying capabilities are essential aspects of any database system as they determine how efficiently and effectively data can be retrieved based on specific criteria. In the context of DynamoDB and MongoDB, their querying capabilities play a crucial role in influencing the overall performance and user experience of applications. 

DynamoDB's primary strength lies in its fast and straightforward key-based lookups. It excels at retrieving individual items based on their primary key attributes. However, DynamoDB's querying capabilities are relatively limited when it comes to complex queries involving multiple attributes or conditions. It supports basic filtering and secondary indexes but lacks the expressive power of MongoDB's query language.  

MongoDB, with its flexible document model, offers a rich and versatile query language that empowers developers to perform complex and dynamic searches. Its expressive query syntax allows for intricate conditions, logical operators, and even geospatial queries, making it well-suited for applications dealing with diverse and evolving data structures. MongoDB's query optimiser efficiently executes queries, and with proper indexing strategies, it can deliver impressive performance even for large datasets.  

When selecting between DynamoDB and MongoDB, the querying requirements of your application should be a key consideration. If your application primarily relies on simple key-based lookups or is deeply integrated into AWS, DynamoDB's speed and scalability may be the right fit. On the other hand, if your project involves complex and flexible data structures with a need for expressive queries, MongoDB's querying capabilities will likely better suit your needs.


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In DynamoDB, Amazon's fully managed NoSQL database, performance is a standout feature. It boasts low latency and predictable response times, making it an excellent choice for applications with strict performance requirements. DynamoDB has high availability and fault tolerance due to its ability to automatically replicate data across multiple Availability Zones, further enhancing its overall performance. 

In terms of query performance, DynamoDB is optimised for simple key-based lookups. For applications that require quick retrieval of data based on specific attributes, DynamoDB excels with its fast read and write operations. Complex queries, however, might require additional effort to implement in DynamoDB. 

On the other hand, MongoDB's performance heavily depends on various factors, including the database schema, indexes, and sharding strategy. Properly optimised, MongoDB can deliver impressive performance for diverse workloads. However, without careful consideration of these factors, MongoDB's performance might not be on par with DynamoDB's predictable and consistent response times.  

MongoDB's flexible schema allows for more straightforward and expressive complex queries. Its support for rich queries with conditions, logical operators, and even geospatial queries provides developers with greater querying capabilities. MongoDB's performance, in this regard, can be well-suited for applications that demand complex and dynamic data retrieval. 

When it comes to consistency models, DynamoDB offers both eventually consistent and strongly consistent reads. This provides developers with a choice between higher throughput with eventually consistent reads or guaranteed data accuracy with strongly consistent reads. MongoDB, too, offers the flexibility of choosing between strong and eventual consistency, allowing users to tailor their database performance to meet specific requirements.  

DynamoDB shines in scenarios where low latency, predictable response times, and seamless scaling are crucial. On the other hand, MongoDB's performance is highly dependent on database design and query optimisation, making it a more flexible choice for projects with evolving requirements and diverse data structures. 

Flexibility and schema 

Flexibility and schema play crucial roles in determining the efficiency and adaptability of a database. In the context of NoSQL databases like MongoDB, flexibility refers to the database's ability to handle diverse and evolving data structures without imposing strict constraints. Unlike traditional relational databases, which require a predefined schema and fixed table structures, MongoDB embraces a schema-less approach, allowing developers to store and modify data on the fly. This gives developers the freedom to iterate rapidly and accommodate changing requirements, making MongoDB an excellent choice for agile and dynamic projects. 

The absence of a fixed schema in MongoDB, however, also introduces the concept of "schema validation," where developers can define rules for data integrity and consistency. This strikes a balance between flexibility and data reliability, ensuring that data remains coherent and accurate while still providing the freedom to experiment and innovate. 

In contrast, DynamoDB adheres to a key-value and document data model, which demands a predetermined schema for data storage. While this can provide better performance for certain workloads, it may limit the agility of developers when dealing with evolving data structures. In summary, MongoDB's flexibility and schema-less design allow for easy adaptation to changing data needs, while DynamoDB's structured schema ensures robustness and predictability in certain scenarios.  

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Indexing capabilities 

Indexing capabilities play a crucial role in the performance and efficiency of any database, including DynamoDB and MongoDB. Indexes are data structures that enhance query speed and help in optimising data retrieval. Both databases offer unique approaches to indexing tailored to their respective data models. 

In DynamoDB, indexing revolves around primary keys. The primary key can be either a single attribute (simple primary key) or a combination of two attributes (composite primary key). DynamoDB automatically creates indexes for the primary key attributes, enabling quick and efficient retrieval of data based on the primary key values. However, DynamoDB does not support secondary indexes, which limits the flexibility in querying data using non-primary key attributes. 

MongoDB, on the other hand, allows for more versatile indexing capabilities. It supports various types of indexes, such as single field indexes, compound indexes, multi-key indexes, and geospatial indexes. These indexes can significantly speed up query execution and enhance the overall database performance. MongoDB also provides secondary indexes, enabling users to query data based on non-primary key attributes, offering more flexibility in data retrieval. 

Both DynamoDB and MongoDB offer indexing capabilities that cater to their unique data models and use cases. DynamoDB focuses on simplicity and automatic indexing for primary keys, while MongoDB provides a more extensive range of index types, allowing for more complex and customised data retrieval operations. Understanding the indexing capabilities of each database is essential in optimising query performance and ensuring smooth operations for your applications. 


In the context of DynamoDB vs. MongoDB, pricing plays an important role in the decision-making process. Both databases have different pricing models that need careful consideration. DynamoDB follows a pay-per-use model, charging based on read and write capacity units, along with storage usage. This flexibility makes it cost-effective for varying workloads and budget-conscious projects. On the other hand, MongoDB offers a more straightforward approach with open-source licensing for the community edition. However, for advanced features and professional support, the enterprise version comes with a subscription-based pricing structure. Understanding the pricing nuances of both databases is essential to avoid unexpected costs and ensure the sustainability of your application in the long run. Businesses must weigh their specific needs and projected growth to make the best-informed decision for their budget and future scalability.

Data consistency and transactions 

Comparing data consistency and transactions in DynamoDB and MongoDB, we encounter notable differences in their approaches. DynamoDB, being a fully managed service, offers two consistency models: eventually consistent reads and strongly consistent reads. Eventually, consistent reads prioritise high throughput, allowing for slightly stale data, while strongly consistent reads guarantee the most up-to-date data, impacting performance. In contrast,  

MongoDB offers a more traditional approach to data consistency. It supports multi-document transactions, providing atomicity, consistency, isolation, and durability (ACID) properties. This allows developers to execute multiple operations as a single transaction, ensuring data integrity. The choice between the two depends on the application's requirements. For scenarios requiring real-time and predictable performance, DynamoDB's eventual consistency might be sufficient. However, applications demanding robust transactional capabilities and ACID guarantees would benefit from MongoDB's transaction support. 

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Use cases  

DynamoDB and MongoDB are both formidable NoSQL databases, each excelling in different use cases due to their unique strengths and features. 

DynamoDB use cases

DynamoDB use cases  

1) Real-time applications: DynamoDB's low latency and seamless scalability make it ideal for real-time applications such as chat platforms, social media feeds, and live gaming leaderboards. It can handle high-throughput workloads with ease, ensuring smooth and responsive user experiences. 

2) Session management: When it comes to handling user sessions in web applications, DynamoDB shines. Its key-value data model allows for quick retrieval of session data, ensuring efficient user authentication and session management. 

3) Internet of Things (IoT): DynamoDB is a perfect fit for IoT applications that generate large volumes of data from connected devices. Its ability to handle massive amounts of sensor data and scale automatically makes it a go-to choice for IoT implementations. 

4) E-commerce and recommendations: E-commerce platforms that demand lightning-fast product lookups and personalised recommendations benefit from DynamoDB's excellent performance and real-time capabilities. 

5) Gaming and scoreboards: Gaming applications, especially those with leaderboards and score-tracking features, can leverage DynamoDB's quick access to user scores and stats for an immersive gaming experience. 

MongoDB use cases

 MongoDB use cases

1) Content Management Systems (CMS): MongoDB's flexible schema and document-oriented structure make it an excellent choice for CMS platforms. It accommodates diverse content types and allows for easy updates and modifications. 

2) Data analytics and reporting: MongoDB's ability to handle unstructured and semi-structured data makes it suitable for data analytics applications, where the data format might evolve over time. 

3) Geospatial data: For applications dealing with geospatial data, such as location-based services, MongoDB's support for geospatial indexes and queries is invaluable. 

4) Prototyping and agile development: MongoDB's schema-free nature enables developers to quickly iterate and prototype applications without being restricted by rigid data structures. 

5) Collaborative tools: MongoDB is well-suited for collaborative tools like project management platforms, where users may have varying data requirements and preferences. 


Making the right decision between DynamoDB vs MongoDB can significantly impact your application's performance, scalability, and development efficiency. Assess your application's needs, consult with your team, and weigh the pros and cons of each database to make an informed choice that aligns perfectly with your project goals. 

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