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
PostgreSQL
Score 8.7 out of 10
N/A
PostgreSQL (alternately Postgres) is a free and open source object-relational database system boasting over 30 years of active development, reliability, feature robustness, and performance. It supports SQL and is designed to support various workloads flexibly.
N/A
Pricing
Amazon DynamoDB
MongoDB
PostgreSQL
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
No answers on this topic
Offerings
Pricing Offerings
Amazon DynamoDB
MongoDB
PostgreSQL
Free Trial
No
Yes
No
Free/Freemium Version
No
Yes
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
—
Fully managed, global cloud database on AWS, Azure, and GCP
—
More Pricing Information
Community Pulse
Amazon DynamoDB
MongoDB
PostgreSQL
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.
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
Other all SQL Databases are based on the traditional Schema Structure and Amazon DynamoDB is NoSQL so you don't need to generate the SQL Schemas. You can store the data whatever you want, whenever you want. You can store data in structured or non-structured any way you want. If …
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 …
Looking into PostgreSQL happened post move to Mongo. Had we considered both options at the time we likely would have went with PostgreSQL. We may migrate at some point in the future but currently it doesn't make sense.
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 …
Both Couchbase and MongoDB are document-oriented NoSQL databases, so they have very similar features. While they do have some fundamental differences in terms of how they scale, shard, etc. the one key reason why we went with MongoDB is its availability and support from the …
The flexible structure underlying MongoDB's construction is not found in other competitors; the ability to easily change the structure without affecting other stored documents. It is very ideal for projects that you cannot predict that the structure will change this way. Of …
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 …
MongoDB is our go-to database solution for any project, and the more we work with it the more we love it. Some say that NoSQL is pointless... Our developers wholeheartedly disagree, because they love working with it. Though both NoSQL and SQL have their purposes, in most …
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 is the best NoSQL database out there. There are others, but Mongo has the largest community, is very easy to set up, and is extremely performant. Compared to a relational DB (like MySQL or Postgres) is like comparing apples and oranges. One isn't better or worse than …
I recently tried out Firestore from the Google firebase family of development products. While it allows structuring of data similar to MongoDB, it handles things a little differently. MongoDB documents are incredibly flexible and can be structured really any way you can …
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.
MongoDB is my only NoSQL database that I have used. I have used SQL databases and don't find them as enjoyable. I code in full stack JavaScript and it blends perfectly with this. I know that there are competitors in this space, and I need to take time to try them all out. I …
I selected MongoDB because it works for well with web interfaces. All of the RDBMS alternatives would have required a lot more time writing schemas and working around retrieving data and mapping it. That could have been somewhat mitigated with Entity Framework, but that again …
PostgreSQL provides both the traditional relational DB setup of MySQLand a more document-driven model like that of DynamoDB. As some of our data is relational and some is document-based, it was more efficient to select the tool that did both than run two, separate databases. …
The features between these database are quite comparable - except for possibly MongoDB. MongoDB being a different type of database and geared towards big data - I don't compare it to PostgreSQL. The other two I have used and would say PostgreSQL does fairly well when compared …
Despite being all open source options, what ended up making us choose PostgreSQL was the robustness of its core, which allows the great workflow that can support timely and efficient response to the demand and demand for resources. In the case of MongoDB, it is a non-relational …
MySQL is a popular open-source alternative to PostgreSQL, but in my experience it lacks the robustness, durability, and flexibility of PostgreSQL. It has also changed hands frequently, so support isn't the greatest. MongoDB and other NoSQL databases are helpful in certain …
PostgreSQL holds it own against both these options. Some of these DBs are in play for certain needs but the majority are PostgreSQL because of cost and operational performance.
MySQL is an Oracle product which has in itself some known issues due to that (support, contract terms). Based on my knowledge, PostgreSQL support everything that MySQL support (syntax wise) and it adds more improvements and syntaxes that make the life of database engineers and …
First It's open source and it's cost-effective compared to other databases.PostgreSQL can be easily integrated with numerous platforms. It is well known and appreciated so relying on it as our system database can be easily accepted by our customers. And if your developing a …
PostgrPostgreSQL as a transaction db engine against oracle and sql server works well. TPM wise compared to MySQL and MariaDB, on an evan scale. SQL function supports, far outweighs compared to MySQL and MariaDB. PG Extensions allow for flexibiltity and scalability. Allows …
When we were originally evaluating Redshift we ran into some issue with dates. Either way, Postgres is a better choice than Redshift because it avoids vendor lockin. We ended up choosing Postgres over MySQL because it was easier at the time to get a hosted Postgres cluster up …
Much more mature and stable when compared to MySQL with features such as MVCC, complex subquery plans, ORDBMS, and NoSQL support. With Oracle retaining rights to MySQL its future as an open database is less secure and is no longer in the hands of the community. PostgreSQL also …
We selected PostgreSQL due to the number of employees who have used it in the past. The data consistency guarantees. The multiple transaction isolation levels support.
PostgreSQL outperforms every other option. It is faster, more flexible, more reliable, easier to maintain, and more consistent in behaviour than any of the other offerings.
It's a viable alternative, with a rich feature set and a reliable system. PostgreSQL is one of the best RDBMS's currently on the market in 2020, it serves just as well as a starter, PoC DB for any software idea as a final, highly valuable database solution for big systems.
PostgreSQL is the proper tool when data consistency matters and other BASE or document-based databases are simply improper. I think PostgreSQL has a fantastic system of slony replication, triggers, and other data maintenance functionality that other databases generally don't …
Compared to MySQL, it works well if you need to extend to your use case Compared to Spark, it works better w.r.t development time in a central database setting Like Redis, it cannot be used for caching and quick access of non-structured data
As I said, Postgres and MySQL are open source which is important for small start ups. Oracle is EXPENSIVE :) Postgres is faster than MySQL (Big factor) MySQL supports replication which makes it more scalable.
I am currently using MySQL and it is difficult to notice much of a difference at all. For free relational databases, there hasn't been enough for me to choose a clear winner. If you're already using a free solution, there would be no reason to change. In terms of comparing to a …
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.
PostgreSQL is best used for structured data, and best when following relational database design principles. I would not use PostgreSQL for large unstructured data such as video, images, sound files, xml documents, web-pages, especially if these files have their own highly variable, internal structure.
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.
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.
Postgresql is the best tool out there for relational data so I have to give it a high rating when it comes to analytics, data availability and consistency, so on and so forth. SQL is also a relatively consistent language so when it comes to building new tables and loading data in from the OLTP database, there are enough tools where we can perform ETL on a scalable basis.
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.
The data queries are relatively quick for a small to medium sized table. With complex joins, and a wide and deep table however, the performance of the query has room for improvement.
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.
There are several companies that you can contract for technical support, like EnterpriseDB or Percona, both first level in expertise and commitment to the software.
But we do not have contracts with them, we have done all the way from googling to forums, and never have a problem that we cannot resolve or pass around. And for dozens of projects and more than 15 years now.
The online training is request based. Had there been recorded videos available online for potential users to benefit from, I could have rated it higher. The online documentation however is very helpful. The online documentation PDF is downloadable and allows users to pace their own learning. With examples and code snippets, the documentation is great starting point.
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.
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.
Although the competition between the different databases is increasingly aggressive in the sense that they provide many improvements, new functionalities, compatibility with complementary components or environments, in some cases it requires that it be followed within the same family of applications that performs the company that develops it and that is not all bad, but being able to adapt or configure different programs, applications or other environments developed by third parties apart is what gives PostgreSQL a certain advantage and this diversification in the components that can be joined with it, is the reason why it is a great option to choose.
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
Easy to administer so our DevOps team has only ever used minimal time to setup, tune, and maintain.
Easy to interface with so our Engineering team has only ever used minimal time to query or modify the database. Getting the data is straightforward, what we do with it is the bigger concern.