Amazon Relational Database Service (Amazon RDS) is a database-as-a-service (DBaaS) from Amazon Web Services.
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Couchbase Server
Score 8.6 out of 10
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Couchbase Server is a cloud-native, distributed database that fuses the strengths of relational databases such as SQL and ACID transactions with JSON flexibility and scale that defines NoSQL. It is available as a service in commercial clouds and supports hybrid and private cloud deployments.
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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
Pricing
Amazon Relational Database Service (RDS)
Couchbase Server
MongoDB
Editions & Modules
Amazon RDS for PostgreSQL
$0.24 ($0.48)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for MariaDB
$0.25 ($0.50)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for MySQL
$0.29 ($0.58)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for Oracle
$0.482 ($0.964)
per hour, R5 Large (R5 Extra Large)
Amazon RDS for SQL Server
$1.02 ($1.52)
per hour, R5 Large (R5 Extra Large)
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Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Offerings
Pricing Offerings
Amazon RDS
Couchbase Server
MongoDB
Free Trial
No
Yes
Yes
Free/Freemium Version
No
Yes
Yes
Premium Consulting/Integration Services
No
Yes
No
Entry-level Setup Fee
Optional
Optional
No setup fee
Additional Details
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Fully managed, global cloud database on AWS, Azure, and GCP
The only direct comparison that I have professionally would be from a past life where we ran Microsoft SQL Server on Microsoft Servers, and while I served as a technical liaison between a vendor and my customers, there were constant issues within my customers' technical teams …
Running MySQL RDS was a simpler solution than running standalone MySQL servers as the semi-managed nature of RDS saved us the need to install, maintain, secure, and backup our database servers. Using MySQL RDS was in addition to running MongoDB Atlas workloads and allowed us to …
MongoDB is nosql database and some clients prefer it. In our presentation we try to persuade them to use RDS with its pros and cons. The type of selection depends upon the actual need.
Amazon Relational Database Service solves part of our architecture problem - more inclined towards on-line transactions and simple user data storage - whereas MongoDB is good for storing structured complex data. For most of the requirements we use Amazon Relational Database …
Because we have our whole architecure on AWS cloud so to provide close connectivity we have choose AWS RDS and also due to Features offered by AWS RDS.
Although the Rackspace service is not comparable, even though it is very good, it requires a lot of administration on my part. Regarding Atlas, although it is not the same as RDS in terms of provisioning and administration panel, I mention it because I found it simpler and more …
We have a strong preference for AWS managed services, and we find that RDS offers excellent integration with various AWS services, making it a seamless choice for our infrastructure. Furthermore, RDS supports integration with automation tools such as Terraform, enhancing our …
Installing, configuring, and managing Oracle Database can be challenging, especially for people who are new to Oracle products. Longer learning curves and higher operational overhead can be caused by this complexity. Amazon Relational Database Service is easy to understand and …
Every traditional rational Database requires server installation & accessing needs to be monitored periodically manually. But Amazon provides easy-to-access and monitor health and scale-up and scale-down option just by clicks without adding any additional hardware.
It is more suitable for our data structure and also has a lower management and implementation cost since we don`t have to do everything from scratch. It also offers great integration with other AWS services which makes it really good to work with.
As most of our infra is on AWS it is good that we use the same service provider as we want all of our infra to be in a single service provider for ease of maintaining. also, other services target very specific database engines vs RDS comes with lots of options which is …
Atlas is easier to use, but we selected RDS because of its ability to use Postgres and MySQL as opposed to a document database. Amazon offers a document database service, but ultimately it made more sense architecturally to choose a standard database.
amazon provides wide range of support for multiple database engines so we dont have to look for any other providers integration with aws ecosystem so we can seemlessly use other aws services connected to database aws have data points globally so it if data needs to be in …
Amazon Relational Database Service will probably give you everything you need from a traditional manual DB setup, except everything is managed for you. The only downside is having to pay the premium for the service; however, the trade-off of not having to deal with the …
Though you could get similar functionality using Docker, Amazon RDS offers a more comprehensive SaaS solution.
With Docker, you still need to have an EC2 instance to install the Docker and manage backup scripts using EC2 snapshots or S3. But RDS provides that solution …
Initially, we planned to move everything to Dynamo DB, however, we had our initial architecture with MySQL, so we thought it would be a good option to migrate and use AWS RDS which seemed to be a good idea actually. I feel the security and the placing it in a VPC, is one …
Redshift is massively scalable but has some limitations that we weren't willing to accept (no JSONB). It also has its own distinct flavor of SQL, and there isn't as much content online about Redshift's flavor of SQL versus postgres'. In the end, we just didn't need to kind of …
When we compare MongoDB with the Couchbase, Couchbase performance is higher than the MongoDB product so we purchased Couchbase as our database solution.
Couchbase's server is more scalable than MongoDB, as MongoDB degrades its performance if the number of users grows. Also, Couchbase allows us to integrate more third-party applications, Couchbase’s query language extends ANSI SQL.
While considering NoSQL database options, we evaluated MongoDB and Couchbase. We decided to go with Couchbase as our dabatase of choice primarily because we had previous Couchbase experience within the team and we knew that this existing expertise could reduce the time needed …
We have good experiences with MongoDB, Elasticsearch, and today we expect to be able to improve our products with
Couchbase and in the near future replace 2 products with 1, which will simplify our product architecture.
The project we are developing with Couchbase, was very inconsistent for few years of the beginning. We had to change data model multiple times. We knew this before starting the project. So we had to choose a NoSQL solution. We also wanted a syncing solution. After some research …
Easy to deploy and manage. Clustering and replication is fairly simple and straightforward. According to developers, Couchbase scored higher points compared to the other products that we evaluated.
Experience with DataStax Cassandra was seamless, but the cost and effort to support it was not justified. Also commercial process experience with Couchbase was much better. ActiveSpaces is a good technology for big TIBCO shop, but keeping with the lifecycle of it is not easy. I …
I'm not qualified enough to make a meaningful comparison, but 2 years after, I hear regularly about issues on Mongo from the other teams, especially on the SRE side. On our side, not much to say, except that it works. Ram, CPU, disk behave like expected. Same for bandwidth. …
Couchbase takes most of the best features of products such as Amazon Aurora, DynamoDB, Mongo DB, or Realm IO. It by the way might be lacking a good AWS strategy compared to other solutions. Couchbase has a great field for improvements in establishing specific deployment …
We looked at several different SQL and NoSQL systems. Most were either too expensive, didn't provide the needed functionality, or were too hard to use with the size of our team. We ultimately went with Couchbase because of its performance, horizontal scalability, and price.
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 …
Cassandra: may be better for bigger use cases, in PB range, due to our use cases being slightly smaller, we did not need this, but we highly rely on efficient indexing, and low latency, which seemed to be better based on our testing in Mongodb. Couchbase Server: Document …
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.
Features
Amazon Relational Database Service (RDS)
Couchbase Server
MongoDB
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
If your application needs a relational data store and uses other AWS services, AWS RDS is a no-brainer. It offers all the traditional database features, makes it a snap to set up, creates cross-region replication, has advanced security, built-in monitoring, and much more at a very good price. You can also set up streaming to a data lake using various other AWS services on your RDS.
Best suited when edge devices have interrupted internet connection. And Couchbase provides reliable data transfer. If used for attachment Couchbase has a very poor offering. A hard limit of 20 MB is not okay. They have the best conflict resolution but not so great query language on Couchbase lite.
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.
Automated Database Management: We use it for streamlining routine tasks like software patching and database backups.
Scalability on Demand: we use it to handle traffic spikes, scaling both vertically and horizontally.
Database Engine Compatibility: It works amazingly with multiple database engines used by different departments within our organization including MySQL, PostgreSQL, SQL Server, and Oracle.
Monitoring: It covers our extensive monitoring and logging, and also has great compatibility with Amazon CloudWatch
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.
It is a little difficult to configure and connect to an RDS instance. The integration with ECS can be made more seamless.
Exploring features within RDS is not very easy and intuitive. Either a human friendly documentation should be added or the User Interface be made intuitive so that people can explore and find features on their own.
There should be tools to analyze cost and minimize it according to the usage.
The N1QL engine performs poorly compared to SQL engines due to the number of interactions needed, so if your use case involves the need for a lot of SQL-like query activity as opposed to the direct fetch of data in the form of a key/value map you may want to consider a RDBMS that has support for json data types so that you can more easily mix the use of relational and non-relational approaches to data access.
You have to be careful when using multiple capabilities (e.g. transactions with Sync Gateway) as you will typically run into problems where one technology may not operate correctly in combination with another.
There are quality problems with some newly released features, so be careful with being an early adopter unless you really need the capability. We somewhat desperately adopted the use of transactions, but went through multiple bughunt cycles with Couchbase working the kinks out.
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.
We do renew our use of Amazon Relational Database Service. We don't have any problems faced with RDS in place. RDS has taken away lot of overhead of hosting database, managing the database and keeping a team just to manage database. Even the backup, security and recovery another overhead that has been taken away by RDS. So, we will keep on using RDS.
I rarely actually use Couchbase Server, I just stay up-to-date with the features that it provides. However, when the need arises for a NoSQL datastore, then I will strongly consider it as an option
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.
I've been using AWS Relational Database Services in several projects in different environments and from the AWS products, maybe this one together to EC2 are my favourite. They deliver what they promise. Reliable, fast, easy and with a fair price (in comparison to commercial products which have obscure license agreements).
Couchbase has been quite a usable for our implementation. We had similar experience with our previous "trial" implementation, however it was short lived.
Couchbase has so far exceeded expectation. Our implementation team is more confident than ever before.
When we are Live for more than 6 months, I'm hoping to enhance this rating.
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.
One of Couchbase’s greatest assets is its performance with large datasets. Properly set up with well-sized clusters, it is also highly reliable and scalable. User management could be better though, and security often feels like an afterthought. Couchbase has improved tremendously since we started using it, so I am sure that these issues will be ironed out.
I have only had good experiences in working with AWS support. I will admit that my experience comes from the benefit of having a premium tier of support but even working with free-tier accounts I have not had problems getting help with AWS products when needed. And most often, the docs do a pretty good job of explaining how to operate a service so a quick spin through the docs has been useful in solving problems.
I haven't had many opportunities to request support, I will look forward to better the rating. We have technical development and integration team who reach out directly to TAM at Couchbase.
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.
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.
Amazon Relational Database Service (RDS) stands out among similar products due to its seamless integration with other AWS services, automated backups, and multi-AZ deployments for high availability. Its support for various database engines, such as MySQL, PostgreSQL, and Oracle, provides flexibility. Additionally, RDS offers managed security features, including encryption and IAM integration, enhancing data protection. The pay-as-you-go pricing model makes it cost-effective. Overall, Amazon RDS excels in ease of use, scalability, and a comprehensive feature set, making it a top choice for organizations seeking a reliable and scalable managed relational database service in the cloud.
The Apache Cassandra was one type of product used in our company for a couple of use-cases. The Aerospike is something we [analyzed] not so long time ago as an interesting alternative, due to its performance characteristics. The Oracle Coherence was and is still being used for [the] distributed caching use-case, but it will be replaced eventually by Couchbase. Though each of these products [has] its own strengths and weaknesses, we prefer sticking to Couchbase because of [the] experience we have with this product and because it is cost-effective for our organization.
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.
So far, the way that we mange and upgrade our clusters has be very smooth. It works like a dream when we use it in concert with AWS and their EC2 machines. Having access to powerful instances along side the Couchbase interface is amazing and allows us to do rebalances or maintenance without a worry
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