Couchbase Sync Gateway

I used Couchbase Server  at ViajaNet five years ago, and I am currently using it at Vertiv for cache specification.

I primarily use Couchbase Server  for cache because this cache is very useful and the performance is excellent. I need ultra-performance for risk analysis. In my order flow, I need to obtain more information about the participant and the count of the order that exists, and I require precise information with ultra-performance.

In the order flow, if a participant makes three orders in two seconds, one second, or one minute, it is accounted for, and I have rules for specific times when this participant makes orders.

Couchbase Server is important for performing in several flows that I have in my company.

The best feature Couchbase Server offers is performance.

When I say performance, I mean the way Couchbase Server structures and manages data. I specifically appreciate the performance that Couchbase Server provides.

Couchbase Server has positively impacted my organization, as it is important for the maintenance and security of the flows I have noticed.

Couchbase Server should improve performance and make the process scalable. The integrity of the information is improved by it.

I have been working as a software engineer in my current field.

I feel there is nothing I should improve, and I do not think about improving anything. I did not purchase Couchbase Server through the AWS Marketplace . I do not have advice for others looking into using Couchbase Server. I would rate this product a 10.

Couchbase Server  is my main use case in a product-based company at Amdocs, specifically for an Amdocs billing product where Couchbase Server  is the core backbone of our application, and I am using a multi-node cluster in a very large environment.

In my daily operations, we have our ordering services integrated into the billing product in the telecom sector, and in that service, we use Couchbase Server bucket as in scope and collection; thus, as part of our application flows, we frequently utilize Couchbase Server bucket with N1QL queries.

Couchbase Server is mainly used for the ordering services, supporting whatever the customer purchases and orders, making it the backbone of our software.

Couchbase Server is robust and has self-healing capabilities; whenever a failure happens, it performs failover and rebalancing, providing high availability and ease of use whether it is deployed on a single node or multi-node server, as well as in containers.

When a node failure happens in practice, Couchbase Server automatically performs failover, bringing the node back online, and then we can do rebalancing where all the VBuckets maintain an online state, ensuring end users are not impacted, and high availability is provided, as well as multiple disaster recovery setups for replication through XDCR to avoid service impact for our multiple services and maintaining query, data, and indexing services in my production environment.

Couchbase Server provides multiple services including event services and supports both JSON and non-JSON data formats, which is a significant aspect of Couchbase Server.

Since working with Couchbase Server starting from version 6, where only scopes and collections were available, I have seen numerous improvements with the release of version 7, especially as bugs identified in version 6 were addressed by the Couchbase Server team.

One specific outcome I have noticed is identifying constraints such as not being able to create more than thirty buckets, which we notified the Couchbase Server team about, along with several missing monitoring parameters; they worked with us to provide better solutions in terms of monitoring and the metrics we require for our production monitoring setups, ensuring our production is up and running smoothly.

Regarding improvements, I feel that the current dedicated monitoring, such as CMX offered by Couchbase Server, requires enhancements for better identification of issues, especially since the market is moving towards AI-driven solutions, and I believe Couchbase Server is working on improving its AI capabilities.

Couchbase Server requires more enhancement, particularly with the MCP launched by Couchbase Server which is not yet fully functional and offers limited tools that are not beneficial for automating production workflows.

Regarding the accuracy and reliability of Couchbase Server's AI capabilities, I perceive there are gaps since the tools are not being developed maturely and require more developed solutions for our agent workflows to facilitate automation with AI agents.

I have been using Couchbase Server for the last five and a half years in my day-to-day operations.

In terms of governance and security, I see Couchbase Server as highly secured, using SSL certificates and allowing for controlled user access without any noticeable gaps in security, though I do suggest vigilance as the AI-driven market develops.

Couchbase Server is currently deployed in both the public cloud and on-premises; in the public cloud, we have deployed it within Kubernetes , while on-premises, it is on a server level.

We are using AWS  for our public cloud deployment.

We have indeed purchased Couchbase Server through the AWS Marketplace .

Monitoring and AI-driven enhancements are critical as the market transitions to AI.

I would advise others considering Couchbase Server to note that compared to other tools such as MongoDB, it is easier to use, reliable, and offers high availability while continually adding more features, making it a suitable choice, especially for large data environments.

I have shared all my thoughts regarding Couchbase Server, including its beneficial aspects and current limitations, and I believe a focus on these limitations will allow end users to take full advantage of the product. I would rate this product a seven out of ten.

Couchbase Server  serves as the primary use case for our real-time web and mobile application backend with caching. We utilize Couchbase Server  for microservice session isolation in our Java Spring Boot  backend, leveraging Couchbase CrudRepository to instantly save and retrieve complex JSON user profiles. Couchbase Server's memory-first architecture handles our high-volume read-write workloads incredibly fast.

We use Couchbase Server to manage user sessions, a dynamic product catalog, and real-time telecom-style insights where microsecond data persistence and sub-millisecond query response are strictly required.

One of the best features that Couchbase Server offers is scalability due to Multi-Dimensional Scaling. Unlike other NoSQL databases where every node has to run every service, this MDS allows us to scale services independently.

Multi-Dimensional Scaling has helped our team by making scaling easier in a noticeable way. If our data volume grows, we scale the data service. If we are running heavy lookups, we horizontally scale just the index or query services.

Couchbase Server has positively impacted our organization by successfully eliminating our data layer silo issues. Instead of stitching together a separate database, a separate caching tier, and a text search platform, Couchbase Server handles operational data, indexing, and vector searching inside one unified data platform. This saved our infrastructure team immense maintenance overhead.

The administrative dashboard web UI of Couchbase Server could benefit from more interactive visual mapping tools for complex, multi-document transactional tracking, which would help in simplifying the debugging option for newer database administrators.

I have been using Couchbase Server for about three years to manage high throughput, low latency NoSQL data layers for our production systems.

Couchbase Server is an absolute powerhouse for high-velocity modern enterprise applications and bridges the gap between flexible schema advantages of NoSQL and the rigid, trusted query capabilities of relational databases beautifully.

I would recommend taking full advantage of Multi-Dimensional Scaling during the initial infrastructure sizing. Isolate the query, index, and the data workload into a separate node. This ensures your heavy operational reads and writes never choke your search indexes, maximizing your cluster performance from day one.

We run Couchbase Server in a hybrid multi-cloud architecture. We use Couchbase Server on our private, on-premises servers for sensitive data compliance, but we also run it inside our public cloud environments on Amazon EC2  instances. This allows us to sync data effortlessly from our localized database up to the public cloud using Couchbase Autonomous Operator for Kubernetes .

I purchased Couchbase Server through the AWS Marketplace . I would rate this product a nine out of ten.