A Database Management System (DBMS) empowers users with the ability to seamlessly create, extract, customize, delete, or even run queries in the data within the database to capture events occurring in the applications or websites. The two most common yet popular non-traditional relational databases are MongoDB and DynamoDB.
DynamoDB, an integral part of the AWS ecosystem, boasts seamless scalability and integration. MongoDB, known for its flexibility, offers a versatile document-oriented approach. It is important to explore DynamoDB vs. MongoDB and illuminate their distinctive features, strengths, and considerations for making a sound choice.
What is MongoDB?
MongoDB is an advanced NoSQL database service that can be used across various operating systems. It was released in February 2009 by Eliot Horowitz, Dwight Merriman, and Kevin P. Ryan.
MongoDB is an open-source, non-relational database. It is specifically designed to store and handle large amounts of data in BSON (Binary JSON), a JSON-like structure where documents are semi-structured rather than fully structured. While often used in cloud environments, MongoDB can be deployed on-premises or in hybrid configurations and is compatible with various operating systems, including Windows, macOS, and Linux.
MongoDB can process big data faster in a wide range of formats, stages, and structures. It supports multiple programming languages, including C, C++, and JS.
Features
Here are some key features of MongoDB for your reference.
- Performance Advice: MongoDB enables performance optimization with updates in applications. This provides efficient results by allowing best-on-demand schema design practices.
- Multi-Cloud Clusters: Users can access powerful and resilient applications that leverage multiple clouds simultaneously.
- Load Balancing: Multiple requests can increase the load on the server. However, MongoDB makes multiple requests for clients to access data simultaneously by distributing it on different servers to ensure uniformity. This approach not only maintains consistency of data but also enhances task performance.
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Use cases
Let us know about some of the use cases of MongoDB.
- Personalization: Millions of users can personalize their experience in real-time with MongoDB. It enables them to target offers, sign up for social media networks, or even customize home pages. In addition, users are allowed to run several queries on your data without transforming, loading, or extracting it.
- Content Management: With MongoDB, users can deliver, store, or weave different data types. In addition, users can also construct any feature in one database. This approach can transform the overall cost of building content into 10%.
- Payments: As MongoDB is flexible in handling data, it allows new products to reach the market quickly when a new payment is applied. Here, there is no occurrence of complexity in data, such as data fragmentation. This ensures users have leveraged the agility for the utility of consolidated operational data to upgrade the payment ecosystem of any enterprise.
Pros and Cons
When it comes to DynamoDB vs. MongoDB, MongoDB shows:
- Higher adaptability
- Scalability
- Quick set-up processing
- Higher speed
- Simple and user-friendly environment
However, MongoDB is challenging to use due to certain issues, including:
- Nesting
- Transaction
- Indexing
- Limited data size
- Duplicates
- Higher usage of memory
What is DynamoDB?
DynamoDB is a NoSQL database service provided by Amazon Web Services (AWS). Launched in 2012, DynamoDB is a fully managed database service within the AWS ecosystem, offering users a secure, highly available platform. Queries in DynamoDB are designed for scalability, but they are not entirely without computational overhead. DynamoDB is chosen for its simplicity, scalability, and suitability for various applications, though it does not support traditional SQL joins typically in relational databases.
Features
There are several features of DynamoDB. Let us explore a few of them.
- Auto Scaling for Improved Cost Efficiency: In case of traffic growth or shrink on application, DynamoDB enhances throughput to lodge the load or scales down so users do not have to pay more for unused capacity, thus saving cost.
- ACID Transactions: DynamoDB was created for mission-critical workloads, including ACID. ACID elaborated as atomicity, consistency, isolation, and durability, is needed to perform complex business logic in applications. The database aids in enhancing developer productivity and supports 100 actions per transaction. This ensures scalability, performance, and enterprise benefits.
Use cases
- Duolingo: It is an online learning site with almost 31 billion data objects on its servers. DynamoDB enables data storage and DevOps for Duolingo’s global data that is needed for personalized data.
- Hess Corporation: A company that deals with crude oil and natural gas production needs to strategize business through financial planning, thus using DynamoDB. DynamoDB streamlined the business process by shifting its energy exploration and production project into AWS. In addition, potential buyers’ data is separated from the business system, handling all data efficiently to achieve optimized and well-managed outcomes.
- GE Healthcare: GE is a popular imaging equipment for diagnostics in the medical field. DynamoDB is widely used to enhance customer value by allowing cloud access, computation, and storage.
Pros and Cons
DynamoDB is great in performance and has high scalability. It offers several benefits, like
- Access to control rules
- Time-To-Live (TTL), which enables the deletion of data when expired
- Storage of inconsistent schema items
- Automatic data management
- Persistence to event stream data
However, there are certain complications while using DynamoDB that may include:
- Hard online analytical processing (OLAP)
- Expensive indexing
- Minimal query options
- Limited virtual storage
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MongoDB vs DynamoDB: Head-to-Head Comparison
Here, we have discussed some key comparisons of DynamoDB vs. MongoDB performance and other factors through the table.
|
Parameters |
DynamoDB |
MongoDB |
|
Data Model & Schema |
It relies on the key-value data model. Thus, DynamoDB offers more rigid schema designs |
It provides document-related data models. It is flexible and enables developers to store data in JSON format with distinct structures. |
|
Querying |
There is limited multi-documents query with key lookups |
Here, key lookups, queries, aggregations, and joins are available |
|
Indexing |
Indexing in DynamoDB is complex and limited |
No extra cost is required for indexing in MongoDB. |
|
Data Integrity |
Built-in ACID compliance and key values provide accurate data for unique identifications through DynamoDB. |
MongoDB guarantees data integrity and durability within replica sets, transaction support, schema design, and document validation. |
|
Monitoring and Performance Tuning |
It is a managed database that offers low-latency access. Thus, it is best suited for predictable workloads |
With evolving data structures, MongoDB is ideal due to its complex querying and flexibility |
|
Backup |
Built-in AWS feature that requires very low overhead |
Backup is self-managed if not using MongoDB Atlas |
|
Serverless Instances |
In DynamoDB, the scaling relies not on data storage alone but on I/O demand. Thus, we can scale the instances as high as the demand |
Here, MongoDB Atlas utilizes the sharding 2 strategies to distribute queries into multiple servers for higher scalability |
|
Database Security |
Utilizes AWS Identity and Access Management (IAM) for authentication and access control |
If you’re not using MongoDB Atlas, MongoDB relies on its own authentication system, which includes role-based access control (RBAC) to manage user permissions |
Conclusion
Choosing the right platform becomes challenging when it comes to DynamoDB vs. MongoDB. However, several factors like deployment, functionality, user requirements, storage requirements, and others can be considered to apply the right approach for performing the task efficiently.
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