MongoDB vs DynamoDB: Key Differences, Use Cases

The increase in unstructured data types has led to growing demands for scalable databases capable of handling such data. Known for their speed and flexibility, NoSQL databases are a specialized category of databases that differ from traditional relational databases. NoSQL databases use various data models, such as key-value, document, or graph-based formats, to manage large volumes of semi-structured and unstructured data efficiently.

MongoDB and Amazon DynamoDB are two renowned NoSQL databases in the industry. In this comprehensive MongoDB vs DynamoDB guide, we’ll explore their core functionalities, pros and cons, and ideal use cases, helping you make an informed decision for your data-driven strategy. By the end, you’ll know exactly which database aligns with your business goals.

What is MongoDB?

MongoDB is one of the most widely used NoSQL document databases, known for its horizontal scaling and load-balancing capabilities. This open-source platform, with its range of out-of-the-box features and drivers for 10+ programming languages, is a versatile choice for both on-premises and cloud deployments.

MongoDB Atlas, its fully managed cloud database service, allows you to deploy databases on AWS, Azure, and Google Cloud. It provides you with robust data security and privacy features, empowering your team of developers to build and manage enterprise-scale applications.

Core Features

• Sharding: MongoDB supports horizontal scaling through sharding, where each shard operates independently but collectively forms a unified database. All client operations in a sharded setup are managed by mongos that route queries to the appropriate shard using a shard key. Sharding allows you to handle growing data volumes and application demands without downtime.
  
• Replication: MongoDB allows you to use replica sets to distribute read operations across secondary servers, minimizing risks such as server crashes and hardware failures. A primary server processes all write operations and synchronizes them to a secondary server. If the primary server fails, the secondary server automatically gets promoted.
  
• Indexing: MongoDB indexes are critical for improving your database’s search speed and query execution time. MongoDB offers a wide range of indexes, including support for language-specific sorting and complex data patterns. You can apply these indexes to any document field, including nested arrays.
  
• Support for Ad-hoc Queries: MongoDB supports various ad-hoc query types, such as field-, geo-, and regular expression queries. These capabilities are powered by MongoDB indexes, BSON documents, and MongoDB Query Language. This flexibility facilitates the processing and updating of ad-hoc queries in real-time to boost your data analytics process.

What is DynamoDB?

Amazon DynamoDB is a robust serverless NoSQL database developed to deliver single-digit millisecond performance at any scale. Launched in 2012, it is optimized for workloads requiring consistent, low-latency responses. This AWS database offers a pay-as-you-go pricing model, where you only pay for the resources you utilize.

You can leverage DynamoDB to build modern, serverless applications, as this fully managed database can handle setup, configuration, maintenance, backups, and more. It can also support tables of any size, ensuring consistent performance and high availability. You can further improve your application’s workflow by integrating DynamoDB with other AWS services, such as Amazon S3, AWS IAM, AWS CloudFormation, and AWS Auto Scaling.

Core Features

• Global Tables: DynamoDB global tables are a fully managed solution that eliminates the need to build and maintain your own replication systems. To use these tables, you specify the AWS Regions where your tables should be available. DynamoDB automatically replicates data changes across these regions without any complex failover processes or downtime.
  
• ACID Transactions: ACID (Atomicity, Consistency, Isolation, Durability) transactions are vital for maintaining data integrity and reliability. DynamoDB supports ACID transactions, ensuring that your data meets all the required standards. It also simplifies the execution of coordinated, all-or-nothing changes within and across items and tables.
  
• Change Data Capture: DynamoDB offers near real-time change data capture with two streaming options: DynamoDB Streams and Kinesis Data Streams for DynamoDB. These streams can capture a time-ordered sequence of item-level changes whenever you create, update, or delete application data.
  
• Secondary Indexes: A secondary index is a data structure that contains a subset of attributes from a table. It also has an alternative key for query operations. With DynamoDB, you can create global and local secondary indexes, which allow you to access data using attributes other than the primary key.

Looking to transfer data between MongoDB and DynamoDB? Check out these step-by-step guides:

• MongoDB to DynamoDB
  
• DynamoDB to MongoDB

MongoDB vs DynamoDB: Pros and Cons

Let’s look into the benefits and shortcomings of MongoDB and DynamoDB before we get into the NoSQL database comparison.

MongoDB Pros

• High Performance: MongoDB uses RAM to store the working dataset that comprises your frequently accessed documents and indexes. This speeds up query execution and response times, which is crucial for real-time analysis and swift decision-making.
  
• Document Store: MongoDB organizes data into a flexible, document-based structure, ensuring data integrity during storage and retrieval. Each data version is well-documented, making it easy for you to trace changes and establish accountability.
  
• Dynamic Schema Support: MongoDB supports dynamic schema, allowing you to add new fields of any data type to documents without requiring schema modifications.
  
• Easy Setup: Compared to traditional RDBMS and SQL databases, setting up MongoDB is relatively easier. It integrates well with modern JavaScript frameworks, offering a faster learning curve for beginners.

MongoDB Cons

• Joins: MongoDB does not natively support SQL-like joins, making the process of joining documents complex. Implementing joins is possible through aggregation pipelines and using the $lookup operator, resulting in increased complexity.
  
• Limited Document Nesting: MongoDB restricts document nesting to 100 levels. This limitation can hinder the proper management of large nested data structures.
  
• Index Management: With proper indexing, you can achieve high-speed query performance in MongoDB. However, poorly implemented indexes can degrade performance. Fixing index-related issues is also time-consuming, causing delays in operations.

DynamoDB Pros

• Fast Response Times: DynamoDB delivers single-digit millisecond response, ensuring quick and reliable performance for any data scale. For applications requiring even lower latency, such as gaming or fintech platforms, this NoSQL database offers a DynamoDB Accelerator (DAX). DAX can provide microsecond response times for read-heavy workloads, significantly enhancing performance.
  
• Secure: DynamoDB complies with global security standards, such as NIST, HIPAA, and PCI. It also integrates with AWS Identity and Access Management (IAM), allowing you to establish fine-grained access control over your data.
  
• Scalability: DynamoDB provides you with more flexibility in scaling resources per workload. It supports horizontal and vertical scaling, helping you create and manage tables of any size. With vertical partitioning techniques, you can scale your data beyond the 400 KB maximum item size.

DynamoDB Cons

• Limited Query Options: DynamoDB’s querying capabilities are limited. The database calculates the number of read capacity units consumed based on the item size, whether you request all attributes or just a few of them. This necessitates full table scans without proper indexing, which is resource-intensive.
  
• Cost Prediction: DynamoDB’s on-demand capacity model automatically adjusts resources for unpredictable workloads. However, without proper monitoring and supervision, it can lead to significantly higher costs, impacting your budget.
• No On-Premise Deployment Option: DynamoDB is a cloud-native service that is exclusively available on AWS. This NoSQL database does not offer an on-premise version, restricting its use for organizations with strict data residency requirements.

MongoDB vs DynamoDB: Key Differences

Use Cases: When Should You Choose MongoDB or DynamoDB?

Now that you’ve seen the DynamoDB vs MongoDB comparison for different aspects, let’s take a look at their use cases:

Use Cases for MongoDB

• Content Management Systems: Integrating MongoDB with your content management applications enables you to store and process various types of content, including text, images, audio, and video. You can also build and deploy new features without impacting existing system performance.
  
• Payment Gateways: Banks and financial institutions can build payment gateways using MongoDB. It supports JSON documents, making it suitable for processing the complex data structures found in financial systems.
  
• Healthcare Applications: MongoDB helps healthcare providers build a central database to consolidate medical records from different legacy systems. You can also integrate it with various healthcare devices to update patient information in near real-time.

Use Cases for DynamoDB

• Gaming Applications: Gaming applications can use DynamoDB’s serverless architecture to efficiently store and retrieve player data, leaderboards, session histories, and player progress with high scalability.
  
• Media Streaming Applications: Entertainment companies can use DynamoDB to manage large-scale media and content libraries. They can swiftly process daily customer interactions for watchlists, recommendations, and the latest releases.
  
• Stock Market Applications: DynamoDB’s global tables are well-suited for financial applications requiring real-time, multi-region data replication for trading platforms. This ensures traders and investors access to the latest stock market data across multiple AWS Regions.

Streamline Data Flow Into Your NoSQL Databases with Estuary Flow

The choice of DynamoDB vs MongoDB depends on your organizational data needs. However, the performance of these databases relies on the quality of the data you input. For optimal functionality, the data must be complete, verified, and accurate. You can employ a robust data integration platform like Estuary Flow to achieve this.

Estuary Flow is a real-time data movement solution that enables you to build ETL/ELT pipelines with its extensive library of 200+ connectors. Using this automation tool, you can consolidate and move data between NoSQL databases and other platforms.

Estuary Flow offers no-code source and destination connectors for both MongoDB and DynamoDB. If you are creating ETL pipelines in Estuary, you can execute custom transformations with SQLite and TypeScript. For ELT pipelines, you can leverage dbt to clean and prepare data before loading it into either platform.

Let’s explore a few key benefits of Estuary Flow:

• Change Data Capture (CDC): Estuary Flow offers CDC to keep your source and destination systems in real-time sync. This facilitates the capture of changes at the source system and replication into the destination with an end-to-end latency of less than 100 milliseconds. By capturing a continuous stream of changes, you can keep your data in the destination system synchronized with the source at all times.
  
• Multiple Deployment Options: There are three deployment options in Estuary Flow to meet your varied organizational requirements. While Public Deployment is fully managed, the Private Deployment option gives you full control over your infrastructure. The third option is Bring Your Own Cloud (BYOC), which allows you to deploy Estuary Flow in your cloud environment.
  
• No-code Configuration: Estuary Flow significantly simplifies the integration process, allowing both technically proficient and non-technical team members to use the tool. Building and managing data pipelines is easier because you do not need to write a single line of code to configure any connector. It only takes a few minutes to set up a data pipeline to capture data from the source and move it to the destination.

With Estuary Flow, you can also move data from DynamoDB to MongoDB. Read this blog to learn how to execute this migration.

Conclusion

Both MongoDB and DynamoDB are modern NoSQL databases that effectively overcome the limitations of traditional SQL databases. Through this comprehensive MongoDB vs DynamoDB comparison, you can now clearly distinguish between both platforms, choosing the one that best fits your requirements.

If your organization already uses an AWS ecosystem or you require high performance and scalability for fluctuating workloads, choose DynamoDB. On the other hand, if you want to avoid vendor lock-in or you are seeking on-premise deployment flexibility with rich querying capabilities, go for MongoDB.

Use Estuary Flow for data integrations in MongoDB or DynamoDB. The platform offers minimal latency, real-time and batch processing, multiple deployment options, and many more features. Register now to get started!

FAQs

Why is MongoDB faster than RDBMS?

MongoDB is faster than RDBMS mainly because of its architecture. RDBMS stores data in structured, relational tables and may require joining between multiple tables to retrieve specific data. Conversely, MongoDB has a document model where all related data is stored together. This makes it faster and easier to retrieve data from MongoDB.

What is the equivalent of DynamoDB in Azure?

Azure Cosmos DB is the Azure equivalent of DynamoDB. While both are NoSQL databases, DynamoDB is recognized for its robust performance and scalability features.

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