MongoDB vs DynamoDB

Introduction

Databases are a key component of most applications and websites, storing crucial data such as content, user details, and capturing events within an application. The choice of database technology can significantly influence everything from application architecture to development processes.

In this blog post, we’ll take a close look at DynamoDB and MongoDB, two popular NoSQL databases that offer alternatives to traditional relational databases. We will explore the strengths and weaknesses of each platform, helping you decide if a NoSQL approach is suitable for your application and which of these two databases best meets your needs.

Let’s dive in!

What is MongoDB?

MongoDB is a NoSQL database that stores data in a document-oriented format (BSON). Released in February of 2009 and developed in C++, it was designed for scalability and handling large amounts of unstructured data. It is a semi-structured database that allows users to seamlessly access data from a myriad of programming languages and other data tools. MongoDB is considered a “NoSQL database” meaning that data is not stored in tables, but in documents. These documents are grouped into MongoDB’s ‘collections’ where they can be accessed through APIs. This novel system was a major difference between MongoDB and its competitors when Eliot Horowitz and Dwight Merriman founded MongoDB. 

Pros of MongoDB:

• Flexible Schema: MongoDB allows for dynamic and flexible schemas, making it easier to evolve the data model over time.
• Rich Query Language: It supports a powerful query language, including support for complex queries, indexing, and aggregation.
• Large Community Support: MongoDB has a large and active community providing extensive documentation, tutorials, and community support.

Cons of MongoDB:

• Scalability Complexity: Scaling MongoDB horizontally can be complex and requires careful planning compared to DynamoDB’s built-in scalability.
• Transactions: Although MongoDB has introduced multi-document transactions, they come with some limitations and complexities compared to traditional relational databases.
• Data Size: MongoDB may not be the best choice for very large data sets due to its storage engine overhead and memory use.

What is DynamoDB?  

Amazon DynamoDB is a cloud-native NoSQL database that primarily functions as a key-value store. Unlike traditional databases, it does not support ANSI SQL but uses a proprietary API based on JSON. This API is accessed through AWS SDKs available in multiple programming languages, making it versatile and developer-friendly.

DynamoDB is designed for seamless scalability and high performance, running exclusively on AWS. This cloud-native approach allows it to automatically scale in response to workload demands, without the need for users to manage hardware. It is particularly suitable for applications with unpredictable workloads, such as IoT, social apps, and gaming.

The database supports a schemaless data model, allowing for flexible and dynamic data structures. However, it requires careful planning for horizontal scaling and data modeling to ensure optimal performance and cost-efficiency. DynamoDB’s design prioritizes simplicity, scalability, and integration with the broader AWS ecosystem, making it a robust choice for modern, data-driven applications.

Pros of DynamoDB:

• Seamless Scalability: Designed for virtually unlimited throughput and storage, making it suitable for applications with unpredictable workloads.
• Fully Managed Service: Provided by AWS, reducing overall overhead and ensuring high availability and durability.
• Low Latency: Offers single-digit millisecond latency for read and write operations, making it suitable for latency-sensitive applications.

Cons of DynamoDB:

• Expense: It can be expensive, especially for applications with high throughput or storage requirements due to its pricing model based on provisioned throughput and data storage.
• Limited Query Capabilities: DynamoDB’s query capabilities are more limited compared to MongoDB’s rich query language, which may require denormalization or additional application logic.
• Vendor Lock-In: Using DynamoDB ties your applications to AWS, potentially limiting portability and making it harder to switch to another provider in the future.

MongoDB vs. DynamoDB: What’s the difference? 

Querying

MongoDB has a rich aggregation pipeline that lets you create a data pipeline directly and natively into the database. But DynamoDB only supports key-value queries. If your requirements exceed this limitation, you must export your data into a separate AWS product like Redshift, Opensearch, etc.

Deployment Options

MongoDB offers flexibility in deployment. It can be deployed on-premises, on virtual machines, or in the cloud across various platforms like AWS, Google Cloud Platform, and Microsoft Azure. DynamoDB, however, is limited to AWS, which can be a deciding factor if you’re looking for multi-cloud or on-premises solutions.

Availability

Both MongoDB and DynamoDB can be configured for high availability. MongoDB’s replica sets and DynamoDB’s inherent AWS-managed architecture ensure that both databases can provide reliable uptime and data redundancy.

Support for Joins

MongoDB supports joins through its lookup feature, allowing complex queries across collections. DynamoDB does not support joins natively, which can be a significant limitation for applications requiring relational data operations.

Searching Capabilities

Searching in MongoDB is straightforward and flexible, thanks to its rich query language. DynamoDB requires local secondary indexes (LSI) or global secondary indexes (GSI) for searching, limiting its flexibility compared to MongoDB.

Data Fetch Size

MongoDB does not impose strict limits on data fetch size, though it is recommended to keep fetch sizes manageable for performance reasons. DynamoDB imposes a 1 MB limit on query responses, which can be restrictive in some use cases.

Indexes

MongoDB supports various types of indexes, including compound, geospatial, multi-key, partial, sparse, unique, and text indexes. This extensive indexing capability makes it versatile for many applications. DynamoDB, on the other hand, only supports local and global secondary indexes, which can limit its indexing flexibility.

Aggregation

MongoDB has a robust aggregation framework, allowing complex data processing and transformation within the database. DynamoDB lacks a native aggregation framework, requiring additional tools and services for such operations.

At a Glance: MongoDB vs DynamoDB

Conclusion

While both MongoDB and DynamoDB have their strengths, your choice should be based on your specific requirements. MongoDB offers greater flexibility and functionality for complex queries and aggregations, while DynamoDB provides cost-effective, scalable storage on AWS. Evaluate your application’s needs, including deployment options, query requirements, and budget constraints, to make the best decision.

At Knowi

Knowi is an end-to-end data analytics platform that allows users to connect natively to MongoDB and DynamoDB without any ETL/ELT process. It also enables users with multiple data sources to compare disparate datasets seamlessly. Launched in 2015, Knowi provides advanced BI capabilities to enterprises worldwide. By utilizing data virtualization, Knowi saves its users from cumbersome and time-consuming ETL processes, making it a unique tool in the BI market.

Knowi supports native integration with NoSQL data sources, allowing users to analyze unstructured data directly—a feature not offered by traditional BI tools. As the business landscape continues to evolve, so does Knowi, ensuring our offerings remain cutting-edge and relevant. If you want to elevate your data-driven decision-making, there’s no better time to explore what Knowi has to offer.