Couchbase Server Reviews and Ratings

Use Cases and Deployment Scope

Fraud detection and identity authentication

We have used to store user information for fraud detection and authentication.

We have large user base where we are detecting pattern based on device and other user information to detect fraudulent behaviour of the user.

Couchbase was faster solution to store this information and we are able to fetch whenever we need.

Likelihood to Recommend

We used this for fraud detection as state earlier section.

This work best for all the noSQL usecases, we tried few of the usecase and did the POC.

This work well and we never stuck with the scaling part,

We are able to run this over the large amount of user records.

Use Cases and Deployment Scope

We are using Couchbase for web application development. Our main focus is public safety and disaster management. We have web and mobile platform presence. We had to solve the syncing issue between mobile and web and Couchbase sync gateway was a great help in this scenario. Also, creating custom document id was another big help for filtering data. Using SDK we perform micro data manipulation processes which is faster comparatively.

Likelihood to Recommend

Couchbase sync gateway was a great help in syncing data between different platforms. It resolves conflicts automatically in most cases. Creating custom document id is really a big help for filtering data without making any N1QL queries. Using SDK we perform micro data manipulation processes which is very fast comparing to other DBs.

Use Cases and Deployment Scope

We use Couchbase to save user data - user's PII data like email, first name, last name, etc., and user's behavioral data like click history, segmentation data, etc. PII data is permanent and behavioral data is temporary. There is a limited TTL on the behavioral data bucket. We are currently working on expanding it to save users' authentication data (session-id etc.) and authorization data (entitlement) as well.

Likelihood to Recommend

If you need fast data, Couchbase is the solution. Also, when data is coming from different sources, and the schema is not always the same, Couchbase is a very good solution. Just define a document and save the data as a JSON doc.

Use Cases and Deployment Scope

We use Couchbase at our company as our main production database. The business problem the product addresses is having to deal with the high throughput of big data at our organization and the scope of our use case is from staging environments via pre environments all the way to production and analytical services.

Likelihood to Recommend

Couchbase used to be great for small startups that needed a document database that would expand well with future needs, but in recent years and newer versions, Couchbase took many components such AS XCDR and the K8s controller from the community version and moved them to the paid versions. Also, backup tools are very very poor in the community version.

Use Cases and Deployment Scope

We use Couchbase for two main purposes. First is the NoSQL database to store unstructured data. The n1ql language is very powerful to retrieve these data in every possible way. The second purpose is key/value storage used in caches. We use Couchbase to store serialized java objects representing cached data.

Likelihood to Recommend

Couchbase is very efficient for the key/value storage of serialized Java objects. It provides a very simple way to have a distributed cache with very low latency. Operations are easy and API is very well done. You can define the time to live on each data to provide a powerful cache.

Use Cases and Deployment Scope

We use Couchbase to store user data. We have millions of anonymous user data that need to be served on the front end in a timely fashion and we were looking for a service that can be robust. We were also looking for a flexible data schema. Couchbase was a perfect solution for both.

Likelihood to Recommend

Couchbase is perfect to handle enormous amounts of data, especially when the data does not have a well-defined schema. Couchbase is way faster than an SQL server solution. Also, it can easily be scaled up and scaled-down. High performance in any cloud server. Also, Couchbase technical support is wonderful

Use Cases and Deployment Scope

Couchbase is used by teams in our organization to store and manage customer data. Couchbase allows huge scalability and performance which is why it is the database of choice for many teams within our organization. Couchbase is also very easy to configure and deploy to various teams. This allows my team, to just be on stand-by for any upgrades.

Likelihood to Recommend

Couchbase is well suited for a quick and easy database cluster to be spun up. That does not require a lot of configuration or updating. A god resource for datasets that will not require too much CPU usage. It is less appropriate for larger data sets and anything that may require loads of memory

Use Cases and Deployment Scope

Offline storage of vessel operations by sea fearers. The data syncs to cloud Couchbase and use for analytics.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

CodeFactory company is implementing an Informational system based on microservice architecture. Some services use Couchbase as data storage. These services have a high load stream of data and the system should have no consumption lag of messages from external systems. Our system has a couple of buckets (one per service) and different document types.

Likelihood to Recommend

I'd like Couchbase for it's scalability and liability. It has impressed performance results. I'm not happy always about search/FTS support

Use Cases and Deployment Scope

I am using Couchbase to store project-related data.

Likelihood to Recommend

well suited- N1QL is very simple and also we can categorize the data based on bucket less appropriate: for analytics, it is not suitable.