Last Updated on November 2, 2023 by Hina Rubab
Both DynamoDB and MongoDB are trendy modern alternatives to conventional database systems like MySQL and PostgreSQL.
The main application for both is storing and querying data contained in “documents”—not real-world Word documents, but JSON chunks transmitted back and forth by computer operating sites like twitterforpets.com.
These solutions tend to outperform relational databases when it comes to scaling challenges. Most technology comparison guides evaluate many launch features without considering how they will be used in the real world. Because these comparisons are intriguing, this essay examines the two databases in critical areas and determines an overall winner. If you are interested in MongoDB, then get into the MongoDB course.
Table of Contents
DynamoDB is less flexible than MongoDB. More essential documents can be used. There are more data types to choose from, which works well with geospatial data. It can query any attribute in a document. MongoDB can run on your laptop, continuous integration environment, or even your too-sophisticated Kubernetes cluster that is sapping your engineering team’s productivity.
Atlas, MongoDB’s well-regarded hosted solution, will eventually account for most of the company’s revenue.
MongoDB does not impose severe constraints on query pace or complexity. If you bombard it with many inquiries, it will do its best to respond, slowly strangling your application and aggravating your users. If you try the same thing with DynamoDB, it will either lock your application out or drastically increase your price, depending on the provisioning model you choose for your table. You can learn about it in a MongoDB course.
As a result, MongoDB is better suited to burst workloads and applications that do not require stringent latency. One can use sharding for huge workloads that need to scale beyond the capacity of a single server, but it comes with the drawback of choosing an appropriate shard key.
The performance of DynamoDB is remarkably consistent. Although its data access APIs are straightforward to grasp, data modeling and design can be challenging—especially for individuals who are used to relational databases. Secondary indexes, both global and local, are challenging to work with and must be thoroughly understood before proceeding.
DynamoDB is a maintenance-free, serverless database. It’s a massively distributed system that automatically supplies, scales, and heals itself, as described at Re: Invent 2018. Backups in DynamoDB are straightforward. Configuring point-in-time restorations and timed snapshots is simple. With AWS Backup, you can define policies with other data stores. Restores used to be excruciatingly sluggish, but they’re now significantly faster.
You can install MongoDB in a variety of ways. Outside of the Atlas service or other controlled choices, this can mean “badly,” adding a general element of risk. Even when using Atlas and Sharded MongoDB, replica set failovers due to upgrades, hardware failures, and other causes can be visible in your application. Backup and restore Atlas or MongoDB Ops Manager, provides services for on-prem installations.
MongoDB is much more than just a document database these days. MongoDB Atlas Search is a full-text search engine that works similarly to Elastic search. Like Amazon Athena, you can query data saved directly in S3 with MongoDB Atlas Data Lake. However, the tradeoff is that your data is moved from your AWS account for processing.
You can send changes into DynamoDB Streams and Kinesis Data Streams using DynamoDB. This feature is the starting point for implementing the Change Data Capture pattern, which allows code to be executed asynchronously in response to DynamoDB updates/inserts, similar to stored procedures. Firehose can sync another fully managed solution, data in DynamoDB in real-time, into services like Amazon Elasticsearch and Amazon Redshift using Kinesis Data.
DynamoDB is a battle-tested armored service tank. DynamoDB works with AWS security services, allowing you to use KMS to bring your encryption keys while leveraging VPC Endpoints to keep your traffic private. All data is encrypted in transit and at rest so that you may answer yes to those bothersome questions from security surveys from the 2000s.
MongoDB continues to run with defaults that allow unauthenticated access to data. These permissive defaults have resulted in actual data loss. Many features, such as LDAP and Kerberos authentication, and System Event auditing, are still lacking from MongoDB’s community version.
Any serious DynamoDB shop will spend a significant amount of time managing DynamoDB capacity. Read and write capacity units (RCUs and WCUs). The intricacy of the query determines the number of capacity units needed in each request. For large tables with generally predictable consumption patterns, autoscaling provided capacity works effectively. Reservations can save you a lot of money on RCUs and WCUs, but unlike other services, AWS doesn’t give any software or advice to help you manage your purchases. While On-Demand cannot be reserved and costs far more than provided capacity, it is frequently cheaper for tiny tables or those with very bursty and unpredictable traffic patterns.
DynamoDB charges numerous dimensions outside *CUs, including the total amount of saved data, duplicated data, streaming data, backups, and restorations.
MongoDB can be inexpensive to use and get started with if you have some computational capability on hand. Dedicated hosting expenses will apply to any production-worthy or hosted environment. Most workloads hosted on MongoDB clusters will be less expensive than DynamoDB, but they won’t grow to gigantic scales with a single API call. Atlas has autoscaling, but it’s a little cumbersome to utilize.
Suppose you want to use the protocol but not MongoDB’s implementation. In that case, there are copy-cat protocol-mostly-compatible services like Amazon DocumentDB and Azure Cosmos DB that provide a MongoDB-esque service. Atlas is available on GCP, Azure, and AWS, and MongoDB is independent of any cloud provider.
For applications built on MongoDB, a lift-and-shift method between cloud providers and on-prem, is far easier than for DynamoDB. However, you’re unlikely ever to need to migrate your application, and the value of doing so would be minimal due to the difficulty of switching providers.
Here is a table comparing some key features of MongoDB and DynamoDB:
|Data model||Document-oriented||Key-value store|
|Scaling||Horizontal scaling||Automatic scaling|
|Querying||Rich set of features||Simple queries|
|Pricing||Traditional licensing||Pay-per-use pricing|
|Indexing||Advanced indexing options||Limited indexing options|
|Community Support||Large and active||Smaller compared to MongoDB|
MongoDB and DynamoDB are both NoSQL databases that offer high scalability and flexibility for storing and processing large amounts of data. However, there are some key differences between the two that may influence which one is best for your specific needs.
Firstly, MongoDB is a document-oriented database, while DynamoDB is a key-value store. This means that MongoDB is better suited for storing complex, hierarchical data structures, whereas DynamoDB excels at simple, single-value lookups.
Another significant difference is in their scaling abilities. MongoDB can be horizontally scaled across multiple servers with ease, while DynamoDB is designed to scale automatically without the need for manual sharding.
When it comes to querying, MongoDB provides a rich set of features for querying data, including support for complex join operations and advanced indexing options. On the other hand, DynamoDB only supports simple queries, which can limit its flexibility in certain use cases.
In terms of pricing, DynamoDB has a pay-per-use pricing model based on the amount of data stored and accessed, while MongoDB offers a more traditional licensing model.
Here are the details regarding MongoDB and DynamoDB. Both are helpful in their way, but if you want to make a career in MongoDB, enroll in the MongoDB course Today.