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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Google Cloud Datastore
Score 8.6 out of 10
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Google Cloud Datastore is a NoSQL "schemaless" database as a service, supporting diverse data types. The database is managed; Google manages sharding and replication and prices according to storage and activity.
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 …
Couchbase could outperform it's competition considerably for database reads and writes. Full text searches were still faster in Elasticsearch but this is more of a feature than a base platform requirement for us.
At the time, Couchbase seemed the most mature of the NoSQL products and would allow us to achieve the goal of improving data access times for our products and services, giving the most benefit to our customers. MySQL was starting to be the bottleneck in our system performance …
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.
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 …
Single console for managing multi-cluster and multi-cloud deployment options and [the] ability to secure and isolate database information in a secure environment to prevent undefined access is great. Analyzing and delivering information and fast access and processing data …
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 …
A strategic company, upcoming products, enhanced concepts. Couchbase is a single platform offering many different smaller products together viz Full-Text Search, Analytics, Eventing, Indexing, Querying, Integration with other products.
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. …
We selected Google Cloud Datastore as one of our candidates for our NoSQL data is because it is provided by Google Cloud, which fits our needs. Most of our infrastructure is on Google Cloud, so when we think about the NoSQL database, the first thing we thought about is Google …
If deploying an application in Google Cloud Platform, using Google Cloud Datastore is a no brainer because of the simplicity of setup. Other options would require more setup and configuration, and do not come with the same level of guaranteed uptime as Google Cloud Datastore. …
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.
Using Google Cloud Datastore in conjunction with Google AppEngine was a very seamless integration and much easier than using other datastores since so much of the configuration is abstracted for you. Because of this, creating simple applications is very easy and getting Google Cloud Datastore to power the backend ties everything together. If we were using Google Compute Engine, I'd imagine the same seamless experience would be there as well.
Cluster sizing during the design phase can be improved, especially if the client lacks prior experience. Vendor consultants are very meticulous in order to provide best of class performance and response time, although some more real-world pragmatic approach is often needed.
Couchbase Lite 2 went thru a major revamp, which broke the compatibility of the applications with some features removed and other changed. That needed development teams working to refactor the applications.
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 give Google Cloud a full score because it satisfies our needs so well. We host most of our infrastructure on Google Cloud and using Google Cloud Datastore helps us to solve our NoSQL storage problem. and Google Cloud Datastore is so scalable and elastic. It saves us lots of time to maintain and saves us money.
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.
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 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.
Couchbase could outperform it's competition considerably for database reads and writes. Full text searches were still faster in Elasticsearch but this is more of a feature than a base platform requirement for us.
We selected Google Cloud Datastore as one of our candidates for our NoSQL data is because it is provided by Google Cloud, which fits our needs. Most of our infrastructure is on Google Cloud, so when we think about the NoSQL database, the first thing we thought about is Google Cloud Datastore. And it proves itself.
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
There have been several areas of our application [that] really needed an ACID compliant database (e.g. strong transactional guarantees) that we thought we could work around while using Couchbase. [In my opinion] that turned out to be a poor bet. You need to be certain that the specific characteristics of a NoSQL database fit your problem.
Couchbase does eliminate the need for schema upgrades completely. I.e no downtime or conversion windows as you migrate your data model, adding attributes, etc. This helped with the deployment timeframe associated with DB changes.
The database is (apparently) a bit more of a space/memory consumer than originally anticipated. During deployments, we received constant pressure from Couchbase consulting teams to eliminate/reduce the number of indexes, and this was because any mutations to docs in a bucket must check for impact against all indexes. More recent years have started to address this with their "collections" features, which helps isolate indexes to specific sub-groupings of documents.