MongoDB vs DynamoDB | What is difference between MongoDB and DynamoDB ?

In my previous articles I have given the architecture of MongoDB and DynamoDB too. In this article I would like to brief you on MongoDB vs DynamoDB with diagrammatic representation. In short I would like to give you idea about difference between MongoDB and DynamoDB with real examples. Everyone knows about MongoDB and DynamoDB but MongoDB vs DynamoDB will give you idea to choose the accurate database.

What is MongoDB?

MongoDB is a popular NoSQL database that is used on mainframes, in-house, in hybrid clouds, and as an AWS service. Due to its distributed scalability, strict data validation policies, and extensive monitoring capabilities, MongoDB is best suited for internet-scale applications.

MongoDB vs DynamoDB

What is DynamoDB?

A key-value and document database called DynamoDB is specifically designed for internet-scale mobile, online, and gaming applications that need fast data access. DynamoDB is used by more than 100,000 AWS customers because it makes setup and management simple. No servers or software has to be installed or maintained for DynamoDB.


Data Modeling – MongoDB vs DynamoDB :

Relational databases were not built to handle large-scale applications, whereas NoSQL databases can. In order to do this, NoSQL databases eliminated several relational databases’ slowing joins and aggregations. As your data grows, they also leverage the concept of partitions to help spread data over numerous storage nodes, enabling consistently quick response times.
The principle behind DynamoDB is that it won’t permit you to construct a poor query. The term “poor query” refers to a query that won’t scale. You won’t be able to use DynamoDB to perform operations like joins or aggregations, even if your data is smaller than 10 GB. As a result, the data model becomes stiff. On the bright side, DynamoDB gives you complete transparency. Even if your application grows to be 1000 times larger than it was originally, your performance won’t suffer over time.

MongoDB vs DynamoDB Tabular format :

 Function MongoDB DynamoDB
 Data Model BSON(JSON-like) document Key-value and document data
with JSON suppor
 Index Data is consistent with indexes.
Compound, unique, array,
partial, TTL, geographic,
sparse, hash, and text are
some indexing techniques.
 Indexes are created
independently of data. Global
secondary indexes (GSIs) are
defined whenever a new partition
key and new sort key are used.
When a table is formed, it needs
to have local secondary indexes
(LSIs) specified. It needs to have
the same partition key as the sort
 Data Type More types of data than
DynamoDB. such as
decimal128, integer, timestamp,
double, string, object, array,
binary data, undefined, object
ID, Boolean, date, null, regular
expression, DBPointer,
javascript, symbol, and
javascript with scope
Number, String, Binary, Boolean,
Null, and Scalar data types.
Data types for documents: List
and Map. Set data types include
binary, number, and string sets.
 Monitoring Tracks more than 100 metrics
that can impact performance.
 Having little access to real-time
database behavior.
 Backup Querable backup No backup is required.
ChargeThe fixed pricing model used by
MongoDB requires upfront
payment for supplied services.
For MongoDB Atlas, pricing is
determined by RAM, I/O, and
storage, as well as server and
sysadmin time if you’re hosting
MongoDB yourself. Spending is
dependable, but it might not be
the best solution for varying
With DynamoDB, you pay only
for the services you really use.
This variable pricing model is
based primarily on a throughput
design with additional fees for
services like backup and
restoration, on-demand from
customers potential, streams,
better information capture (CDC),
and others. Your spending could
become less predictable as a
ReplicationReplica sets, which are several copies
of data dispersed among servers, racks,
and data centers, are automatically
maintained by MongoDB.
Data is synchronously replicated using DynamoDB between three locations within an AWS Region.
The Global Table option for DynamoDB Streams is offered by
When to Use?When trying to find a database that can handle key-value workloads.
When with a commitment to AWS and no future plans to change the deployment
When trying to find a database that can handle key-value workloads.
When with a commitment to AWS and no future plans to change the deployment
MongoDB vs DynamoDB

Consistent Operational Maturity :

Because every read/write activity is present in the primary MongoDB replica set and is scaled across several partitions, MongoDB is by default strongly consistent (shards). Read operation consistency can be loosened if necessary. Secondary consistency controls are used to direct read queries to secondary replicas that are within the primary server’s permitted limits.
By design, DynamoDB is eventually consistent. Strongly consistent data is returned by read operations, which increases latency and doubles the cost of the read. When running queries on DynamoDB’s global secondary indexes, there is no assurance that the reading consistency will hold. A GSI operation is consistent, returns outdated or destroyed data, and increases program complexity.

Operational Maturity :

Using built-in operational and security best practices from MongoDB, including as role-based access control, end-to-end encryption, network isolation, VPC peering, and more, users can create, scale, and manage clusters. Because the replica set members are distributed and self-healing, Atlas deployments are assured to be reliable. Due to continuous backups and point-in-time recovery, data loss is not a concern. It supports auto-scaling for both storage and computing capacity with zero downtime during configuration changes. Additionally, you get to see thorough dashboards that provide business insights for monitoring performance in real time and customizable notifications.

I hope I have covered all the points of MongoDB vs DynamoDB with examples. If you like this article or if you have any issues with the same kindly comment in comments section.

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