IBM Cloud Databases are open source data stores for enterprise application development. Built on a Kubernetes foundation, they offer a database platform for serverless applications. They are designed to scale storage and compute resources seamlessly without being constrained by the limits of a single server. Natively integrated and available in the IBM Cloud console, these databases are now available through a consistent consumption, pricing, and interaction model. They aim to provide a cohesive…
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MongoDB
Score 8.7 out of 10
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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
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Pricing
IBM Cloud Databases
MongoDB
Editions & Modules
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Shared
$0
per month
Serverless
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Dedicated
$57
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Offerings
Pricing Offerings
IBM Cloud Databases
MongoDB
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
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Additional Details
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Fully managed, global cloud database on AWS, Azure, and GCP
We use Amazon's RDS (MySQL database), Redislabs (Redis) and also MongoDB's Atlas. They all have their own advantages and disadvantages. For us, MongoDB's Atlas and Compose are obviously similar services. For now, we use Atlas to try new things (since they run the latest stable …
I tried MLab and was not a fan of how the UI worked on their control panel. It felt outdated and cumbersome. They offered less backup solutions for the price point as well. In fact, you had to contact them with a ticket if you wanted access to a daily backup. Anything over that …
MongoDB is the primary db we use, and Meteor is the primary application framework. Configuring MongoDB to fully support Meteor oplog tailing is a challenge - and when we started looking, Compose was those only MongoDB provider that had turnkey support for Meteor.
We previously hosted our own Redis and RabbitMQ cluster. Before switching to IBM Compose we evaluated Redis Lab, Scalegrid, AWS ElastiCache, CloudAMQP and others. We still host our core database (MongoDB) ourselves.
All our databases are hosted on Compose. We haven't seen a reason to switch providers, however, we have compared with some others and Compose seems to be the best from a cost and reliability standpoint.
While at the time, Amazon RDS did/does not create Mongo databases, I was able to set up many with PostgreSQL databases with the same ease as IBM Compose. However, IBM compose does seem to offer a more intuitive application control panel. Amazon RDS costs run on a server …
We selected Compose because we initially thought that they would provide great support, and that they would bring encryption at rest within months. That has not materialized yet.
We also thought that the cost, while far from being the lowest, was reasonable.
We use Amazon Aurora as our primary datastore and use IBM Compose Mongo as an alternative only when Aurora does not cover the use case well. Amazon DynamoDB looks good but doesn't have the same wealth of libraries and support which makes MongoDB easy to use and therefore was …
We have one instance of mLab that has been equally easy to scale as Compose, but with the added benefit of extensive logging and performance monitoring tools, including an index suggester. All modern cloud db providers seem to offer more of this type of functionality at this …
Other options are lower priced, however IBM Compose has by far the best interface for managing and editing data within the database. It also has many forms of databases for us to deploy, beyond what we are currently using. So, in the event we need to add other services, we can …
We initially selected IBM Compose because it was easy to use and cost-effective. We switched to mLab when we need to scale and have dedicated clusters.
Mongo Atlas - at the moment it looks better. It has 3.6 (Compose stuck at 3.4). Lower pricing (it seems). AWS Dynamo DB etc - I decided rather quickly not to use this, mostly for lack of adequate documentation.
We had used mlab at one point but found it less reliable than compose.io. Really, we wanted a strong foundation to start from day one and figured a produced backed by IBM would be the way to go. We are still a small startup and don't have a ton of resource to mess around with …
I love IBM compose for its simplicity, reliability, scalability, and price. I am uncertain on how the infrastructure is deployed and fault tolerance as we rarely need this.
Less Appropriate Scenario: 1) Small Scale or Low Budget Projects 2) Organizations with limited expertise in cloud technologies may find the learning curve steep, especially if they are not familiar with the IBM Cloud platform 3) If database requirements are highly dynamic and change frequently, the comprehensive features and management provided by IBM Cloud Databases might be overkill. A more flexible, self-managed solution could be preferable for adapting to rapid changes.
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.
The ease of setup was effortless. For anyone with development experience, a few simple questions such as name and login data will get you set up.
The web application to manage cluster settings, billing settings and even introspect the data was simple and most importantly worked all the time. This can not always be said for web interfaces of other products.
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.
Better cost reports, before just increasing to another tier, thus increasing the price. This is critical for early stage startups, where budget is tight.
Add more data center options. As a comparison, a similar service, Aiven.io has dozen more options than Compose (basically all big cloud providers). We moved from AWS to Digital Ocean, which made us stop using Compose, since Compose forces us to be either on IBM or AWS.
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.
IBM is our trusted partner which never failed to meet our expectations. Stability, efficiency, usability and security is a must have for our business which is fully provided by IBM Cloud Databases
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.
IBM Cloud Databases' pricing structure is easy to understand, and if you choose the right product, you can operate your system at minimal cost. Although there is ample documentation available, there doesn't seem to be a user community running on it, so specific usage know-how and troubleshooting can sometimes take longer than expected.
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.
Support is helpful enough, but we haven't always had questions answered in a satisfactory manner. At one time we realized that Compose had stopped taking database snapshots on its two-per-day schedule, and had in fact not taken one for many days. Support recognized the problem and it was fixed, but the lack of proactive checks and the inability to share exactly what happened has caused us to look elsewhere for production work loads
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.
The reason why I choose IBM Cloud Databases is that the IBM cloud toolset is already being used in other functions of the company and by using IBM Cloud Databases, the other cloud tools are better embedded and integrated. If the company is set to use amazon tools, I would go for rds.
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.
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