IBM Cloudant vs. IBM Cloud Databases

IBM Cloudant is the best implementation of CouchDB, or any NoSQL database that you could use if you are looking for a database that can handle extremely rapid writes to a database without having to worry about transactional integrity. IBM Cloudant also abstracts out CouchDB's replication/multi-node requirements and ensures high availability on its own. It also allows map-reduce based indexing which will allow massive databases to be aggregated and queried very quickly. It should not be used in cases where you require structured data which is organized according to a schema, or if you want to maintain ACID database properties.

Incentivized

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.

Incentivized

• We had a small data mart project that required the storage of some rather highly connected data that also had a relatively small footprint. This made IBM Cloudant an obvious choice because we could store the data in a data structure that met our project need al while using a platform that our web development team understood and was comfortable with.
• We had a bunch of geospatial data that we needed for analysis. Having GeoJSON being natively supported by Cloudant made it an easy choice.
• Cloudant was cloud-based and didn't require a DBA support it, this allowed the project to move ahead without pushback from the infrastructure team.

Incentivized

• 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.

Incentivized

• To have a sort of LUW - Logical Unit Work when many documents are involved into a single update process. The changing of one document is related to its status information but it must be synchronized with all the other documents involved in the process.

Incentivized

• 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.

Incentivized

the flexibility of NoSQL allow us to modify and upgrade our apps very fast and in a convenient way. Having the solution hosted by IBM is also giving us the chance to focus on features and the improvement of our apps. It's one thing less to be worried about

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I am very fond of the features and reliability of the compose DaaS, however I could switch if I find same qualities for lower price.

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It's mostly just a straight forward API to a data store. I knock one off for the full text search thing, but I don't need it much anyways. Also, the dashboard UI they give is pretty nice to use. It provides syntax-highlighting for writing views and queries are easy to test. I wish other DBs had a UI like this.

Incentivized

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.

it is a highly available solution in the IBM cloud portfolio and hence we have never had any issues with the data base being available - we also do continuous replication to be on the safer side just in case some thing goes awry. We also perform twice a year disaster recovery tests.

Incentivized

very easy to get started and is very developer friendly given that it uses couchDB analytics. It is a cloud based solution and hence there is no hardware investment in a server and staging the server to get started and the associated delays/bureaucracy involved to get started. Good documentation is also available.

Incentivized

Very happy by the commitment given by the team which has been really good over the last 7 years of usage.

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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

Incentivized

online resources are good enough to understand but there is nothing like testing. In our case, we discovered some not documented behavior that we take in count now. Also, the experience in NodeJs is critical. Also, take in count that most of the "good practices" with cloudant are not in online courses but in blogs and pages from independent developers

Incentivized

• Test the architecture on CouchDB helped us to address initial design flaws.
• The migration to Cloudant as such was very painless.
• We have migrate our replication system to Cloudant Android Sync for mobile devices.
• We have regular informal contact with the Cloudant leadership to discuss our use cases and implementation strategies.

Incentivized

MongoDB Atlas and Azure Cosmos DB are the closest competitors we found with Cloudant, especially in terms of fixed pricing and having a GUI for easy viewing and quick edits of data. Cloudant's pricing model flat out beats MongoDB Atlas' in terms of how easy it would be to predict costs. Cosmos DB is a much closer competitor, as it integrates well with Azure's stack similarly to Cloudant and the rest of the IBM Cloud stack; similar [throughout]-based pricing and replication options; and even the GUI and ease of query using SQL, which my team and I were more familiar with. Where Cloudant beats out Cosmos DB is again having a more simple pricing model (ops/sec vs Cosmos' "request units" voodoo) and being based on open-source software assuaging fears of vendor lock-in.

Incentivized

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.

Incentivized

The service scales incredibly well. As you would expect from CloudDB and IBM combination. The only reason I wouldn't score it a 10 is the fact that document trees can get nested and nested very quickly if you are attempting to do very complex datasets. Which makes your code that much more complex to deal. Its very possible we could find a solution to this problem with better database planning to begin with, but one of the reasons we chose a service over a self-hosted solution was so we could set it up quick and forget about it. So we weren't going to dedicate a team to architecture optimization.

Incentivized

• Saving in-terms of cost of procuring and maintaining hardware, which will be realized over the next 5 years.
• Positive ROI in terms of the number of FTEs involved in maintaining our databases; our DBAs can now focus on other important and business critical applications.
• Best ROI in terms of our organization's vision - they are no longer anxious / nervous to move to the cloud. We are already on the CLOUD.

Incentivized

• Prove use cases prior to administering entire platform, obtain ROI faster
• Able to achieve the technological components of our advanced analytics team without full scale purchase of AI platform
• Developed several studies to prove out cloud Db value, speed to deploy

Incentivized