Monitoring platform for keeping systems up and running at all times.
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Detect and resolve any incident in record time.
Conform to industry best practices.
Both DynamoDB and MongoDB are NoSQL databases, but the similarities probably end there. In this article, we cover their strengths and weaknesses in 8 basic categories, so that you can decide which one suits best your needs.
While the data model behind Mongo is more flexible for storage and retrieval, Dynamo is stronger in terms of scalability, consistent performance under heavy load, and infrastructure abstraction.
Mongo also offers Atlas, a managed infrastructure, similar to AWS RDS for running MongoDB Clusters. Nonetheless, Mongo Atlas is not serverless and does not simplify operations as much as DynamoDB. It is still the closest comparison we can make, though.
Below we outline the eight basic differences in how they perform in several categories to support the decision on which one would suit best each of your use cases:
Dynamo follows a key-value store data model with tables and items. Data is organized in tables, which contains items. Each item contains a set of key-value pairs of attributes. Only indexed key-values can be queried and there is a limit in how many indexes can be built. Performance remains practically unchanged regardless of the query structure and database size.
Mongo, on the other hand,is centered around collections of documents. Virtually any data point can be queried on Mongo. Performance can vary a lot depending on query complexity and database size.
Both databases support ACID transactions.
MongoDB is schema-free, but it still possible to enforce a schema, if needed. DynamoDB is schema-less, meaning it’s impossible to enforce any kind of schema on the database side.
This difference can be positive or negative depending on your use case.
As part of the AWS ecosystem, DynamoDB provides great integration with other cloud services. Integrating MongoDB requires considerably higher development and maintenance efforts.
Building event-driven architectures using queues and serverless functions, for example, in connection with your data storage is much easier with Dynamo in AWS.
DynamoDB offers high scalability by relying on HTTPs API endpoints. Mongo, on the other way, still requires socket connections, which can be an additional source of bottlenecks in your database infrastructure.
The concurrency model is relatively simple in DynamoDB and performance is predictable no matter how you use the database. It is much easier to cross-check the database capacity versus your application demand.
Because of its flexible querying and data modeling, Mongo will probably require a load performance analysis for each case.
For small to medium-sized applications, MongoDB Atlas would probably meet most throughput and storage scalability requirements. For projects that expect high increases or unpredictable demand, DynamoDB might be the safest option to cope with growth.
DynamoDB provides Multi-AZ and Multi-Region data replication out of the box, 100% managed by AWS. All the logic and processing for data distribution are also taken care of internally by AWS.
Although MongoDB supports multi-node clusters, it is not trivial to deploy and manage it, especially on a large scale. Mongo Atlas simplifies the process of deploying highly available clusters, which can even run on AWS itself. Still, it does not compare to how simple DynamoDB is.
Backup options are quite similar in both services and would meet the needs of most cloud applications. They will provide options for continuous, point-in-time, and snapshot-based backups.
For being an open-sourced database, MongoDB wins big here, as it can run on virtually any cloud, traditional hosting platforms, and on-prem. Dynamo, on the other hand, is a proprietary database that cannot be deployed outside of AWS.
Both services provide a level of security that should be enough for most production cloud deployments. Applications in finance or health data industries might need extra security and certifications that might require a specialized analysis that goes beyond the purposes of this article.
Some notable companies reported to use the databases in their projects:
In case you would like to explore a more in-depth comparison between Dynamo, Mongo, and Atlas, check out these Learning Center articles: DynamoDB Vs. MongoDB and DynamoDB Vs. Mongo Atlas.
In this article, we’re covering 4 tips for AWS Lambda optimization for production. Covering error handling, memory provisioning, monitoring, performance, and more.
In this article we’ll go through the ins and outs of AWS Lambda pricing model, how it works, what additional charges you might be looking at and what’s in the fine print.
Dashbird was born out of our own need for an enhanced serverless debugging and monitoring tool, and we take pride in being developers.
Dashbird gives us a simple and easy to use tool to have peace of mind and know that all of our Serverless functions are running correctly. We are instantly aware now if there’s a problem. We love the fact that we have enough information in the Slack notification itself to take appropriate action immediately and know exactly where the issue occurred.
Thanks to Dashbird the time to discover the occurrence of an issue reduced from 2-4 hours to a matter of seconds or minutes. It also means that hundreds of dollars are saved every month.
Great onboarding: it takes just a couple of minutes to connect an AWS account to an organization in Dashbird. The UI is clean and gives a good overview of what is happening with the Lambdas and API Gateways in the account.
I mean, it is just extremely time-saving. It’s so efficient! I don’t think it’s an exaggeration or dramatic to say that Dashbird has been a lifesaver for us.
Dashbird provides an easier interface to monitor and debug problems with our Lambdas. Relevant logs are simple to find and view. Dashbird’s support has been good, and they take product suggestions with grace.
Great UI. Easy to navigate through CloudWatch logs. Simple setup.
Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.
End-to-end observability and real-time error tracking for AWS applications.