Amazon DynamoDB is a cloud-native, NoSQL, serverless database service.
$0
capacity unit per hour
MongoDB
Score 8.9 out of 10
N/A
MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
$0.10
million reads
Oracle Database
Score 8.3 out of 10
N/A
Oracle Database, currently in edition 23ai, is a converged, multimodel database management system. It is designed to simplify development for AI, microservices, graph, document, spatial, and relational applications.
$0.05
per hour
Pricing
Amazon DynamoDB
MongoDB
Oracle Database
Editions & Modules
Provisioned - Read Operation
$0.00013
capacity unit per hour
Provisioned - Write Operation
$0.00065
capacity unit per hour
Provisioned - Global Tables
$0.000975
per Read Capacity
On-Demand Streams
$0.02
per 100,000 read operations
Provisioned - Streams
$0.02
per 100,000 read operations
On-Demand Data Requests Outside AWS Regions
$0.09
per GB
Provisioned - Data Requests Outside AWS Regions
$0.09
per GB
On-Demand Snapshot
$0.10
per GB per month
Provisioned - Snapshot
$0.10
per GB per month
On-Demand Restoring a Backup
$0.15
per GB
Provisioned - Restoring a Backup
$0.15
per GB
On-Demand Point-in-Time Recovery
$0.20
per GB per month
Provisioned - Point-in-Time Recovery
$0.20
per GB per month
On-Demand Read Operation
$0.25
per million requests
On-Demand Data Stored
$0.25
per GB per month
Provisioned - Data Stored
$0.25
per GB per month
On-Demand - Write Operation
$1.25
per million requests
On-Demand Global Tables
$1.875
per million write operations replicated
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Oracle Base Database Service - Standard
$0.0538
per hour
Oracle Base Database Service - Enterprise
$0.1075
per hour
Oracle Base Database Service - High Performance
$0.2218
per hour
Standard Edition
Contact Sales
Enterprise Edition
Contact Sales
Personal Edition
Contact Sales
Offerings
Pricing Offerings
Amazon DynamoDB
MongoDB
Oracle Database
Free Trial
No
Yes
Yes
Free/Freemium Version
No
Yes
Yes
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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Fully managed, global cloud database on AWS, Azure, and GCP
—
More Pricing Information
Community Pulse
Amazon DynamoDB
MongoDB
Oracle Database
Considered Multiple Products
Amazon DynamoDB
Verified User
Director
Chose Amazon DynamoDB
More flexible and easier to get started with than RDS, but, in my opinion, much worse monitoring/cost and query/modeling complexity than MongoDB
MongoDB vs. Amazon DynamoDB:• MongoDB requires more human management than DynamoDB, which is a fully managed service.• DynamoDB's scalability is automatic, whereas MongoDB's horizontal scaling may require more work.• When compared to DynamoDB, MongoDB offers more extensive data …
DynamoDB provided an easy to use, schema-less, out of the box solution that can be used to spin up a full working implementation very easily. It doesn't require extra knowledge such as MongoDB query functions
Lesser flexibility but better performance, and more predictable development support are the key points where Amazon DynamoDB comes out on top, when compared to MongoDB.
We are always assembling our solutions on AWS and DynamoDB is a better fit for us because of its simplicity. DynamoDB has its ow sets of triggers that make this an integrated solution on AWS. Besides, we wanted to use a key-value solution for our simple edge DB, and we didn't …
DynamoDB's scalability is more automated and effortless, making it easier to handle rapid growth. Other tools require more manual configuration while DynamoDB simplifies database administration. Also, DynamoDB provides strong consistency while other tools like MongoDB and Apache…
MongoDB has some performance issues and can get corrupted from time to time and has needed to be rebuilt. We have not had that experience while using DynamoDB.
MongoDB was basically the first approach we used but because there was concern that some data may miss we were reluctant to use it. Oracle Database and SQL Server was our second approach but it was throttling so in last we tested out Amazon DynamoDB and it met our requirement.
For our use case, we needed a noSQL that would work with AWS Lambdas of specific parts of the internal web applications. We optimized billing and uses , diversified databases for various parts; so it’s not very expensive.
Verified User
Engineer
Chose Amazon DynamoDB
i think both depends on usuability and app requirement
Performance at high scales is better and the cost at high scales is less. If one has a ton of data generated and has to work their way through it, I think Amazon DynamoDB should the go-to database. There are no compromises when it comes to performance at a huge scale. With any …
The Amazon Web Services managed Amazon DynamoDB has excellent features which makes it stand out from all the others in market right now. The management ease it offers is far superior than its competitors and on top of that the on-demand pricing model is an advantage which works …
DynamoDB offers strong consistency, more fine-grained control over read and write capacities, and integrates seamlessly with other AWS services. DynamoDB is designed for horizontal scalability and high throughput, making it a better choice for applications with rapidly changing …
The automation is much more subtle and it performs way better for internet-scale applications. No matter the number of connections, the performance doesn't dip even a bit.
Mongo services are outside of our Vpc and are on a different network. Since most of our infra is on AWS, dynamo by AWS was a natural choice. Most of our engineers are familiar with AWS sdk and the console so that brought in a much smaller learning curve for our engineering team
It seamlessly integrates with Lambda, simplifying the deployment and management of serverless architecture. Both Lambda and DynamoDB are designed are highly scalable. Lambda functions can be triggered by various AWS services and events, such as changes in DynamoDB tables which …
MySQL is a great for querying related data, but it's unable to store structured data and has a fixed schema. Also SQL can be non-intuitive. DynamoDB, CouchDB and Redis all make querying the data quite difficult and lack important features. The problem CouchDB tries to solve is …
In our early development days we weighed NoSQL databases like MongoDB with RDBMS solutions like MySQL. We were more familiar with MySQL from past experience but also were wary of painful data migrations that slowed down development iterations and increased the risk of outages …
Your default choice should not be MongoDB in my opinion. Most user-facing systems are relational by nature so a well known and reliable SQL database would be easier to maintain and simpler to develop long term. If you highly value speed of development go with Firebase. If you …
It does not belong to certain cloud platforms. MongoDB is an independent program that works with any cloud platform including Amazon Web Services and the Google Cloud Platform. For companies who want to maintain a cloud agnostic structure, MongoDB is a great choice for NoSQL …
We tend to choose MongoDB when we're faced with a particular situation: we know that we need a NoSQL database in general, and want an open-source implementation that allows us to prevent against platform lock-in. Amazon's new DocumentDB product even allows us to choose to use …
MongoDB was the most full-featured NoSQL database we evaluated - that offered atomic transactions at a document level, built-in HA & DR, open source, robust queries, and enterprise level support.
Other platforms had specific parts of what we were looking for - MongoDB had it all.
Relational DB are not efficient when storing data structure like JSON. Different data structure can be stored without defining the schema. Most relational db might store data like Json as blobs. One single entry would store the entire JSON as blob and you can't query the …
We chose MongoDB because it fit our specific use cases better than the other two NoSQL products that I've identified. There are some use cases where those products would be better. Be sure to use the right tool for the job, for us, it was MongoDB, for you it might not be.
Oracle Database
Verified User
Team Lead
Chose Oracle Database
I have selected Oracle database from other databases as this database is relational database which stored the data in structural and tabular format which is better than any other databases which I have used in my carrier. Also MongoDB is no SQL database where we can use SQL …
Oracle is placed in a good spot against its competitors. It has advantages over its competitors in its legacy stability and high availability. A common engine to handle relational, JSON, Vector, and graph data makes it more cost-effective. Given all the good features, the …
The Oracle database was selected before I started working on the project, so I can't tell the reasons behind the choice. However, it was recognized as the best suited for holding several million records for related entities and was preferred over NoSQL options.
Oracle 12c is good for all business applications but still consider Microsoft SQLServer for internal/other applications to save on cost.
Verified User
Administrator
Chose Oracle Database
Exadata is expensive and we decided to switch to 12c for the sake of consolidating and keep up with Oracles initiative to move towards cloud. Maybe in the future.
Oracle is serving a different segment with respect to those above. Till now, we have stuck with Oracle and continued to support our internal customers. Other DBs are serving a few targeted applications by a few application teams.
We currently use all of the above database technologies in different applications, but where the application is mission-critical we use Oracle. Microsoft SQL Server is good for canned applications such as back-office or HR. MySQL does not have the same level of logging or …
Oracle 12c is able to support daily operations that might be required. Of course, Exadata is a powerful machine that is definitely fit for big scale but requires high demand of both CPU, IO, and memory processing. Also the one engineered system saves lots of time to build the …
It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad
If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
We migrated from NoSQL to an Oracle database. One of the reasons was robust backup and recovery options available in the Oracle database, which provide zero data loss. A transactional database like Oracle is a better fit for our use case than NoSQL. On a large scale, deployment was evaluated as a cheaper option than the NoSQL engine. This conclusion came even after considering Oracle license is expensive.
Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
An aggregate pipeline can be a bit overwhelming as a newcomer.
There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.
I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
There is a lot of sunk cost in a product like Oracle 12c. It is doing a great job, it would not provide us much benefit to switch to another product even if it did the same thing due to the work involved in making such a switch. It would not be cost effective.
Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.
NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
Many of the powerful options can be auto-configured but there are still many things to take into account at the moment of installing and configuring an Oracle Database, compared with SQL Server or other databases. At the same time, that extra complexity allows for detailed configuration and guarantees performance, scalability, availability and security.
It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.
Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
1. I have very good experience with Oracle Database support team. Oracle support team has pool of talented Oracle Analyst resources in different regions. To name a few regions - EMEA, Asia, USA(EST, MST, PST), Australia. Their support staffs are very supportive, well trained, and customer focused. Whenever I open Oracle Sev1 SR(service request), I always get prompt update on my case timely. 2. Oracle has zoom call and chat session option linked to Oracle SR. Whenever you are in Oracle portal - you can chat with the Oracle Analyst who is working on your case. You can request for Oracle zoom call thru which you can share the your problem server screen in no time. This is very nice as it saves lot of time and energy in case you have to follow up with oracle support for your case. 3.Oracle has excellent knowledge base in which all the customer databases critical problems and their solutions are well documented. It is very easy to follow without consulting to support team at first.
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
Overall the implementation went very well and after that everything came out as expected - in terms of performance and scalability. People should always install and upgrade a stable version for production with the latest patch set updates, test properly as much as possible, and should have a backup plan if anything unexpected happens
The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
Because of a rich user base and support for any critical issue, this is one of the best options to choose. In case the project has a TCO issue, it can compromise and choose Postgres as the best alternative. SQL server is also good and easy to code and maintain but performance is not as good as the Oracle
I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.
Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.
It has allowed us to add more scalability to some of our systems.
As with any new technology there was a ramp up/rework phase as we learned best practices.
Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB