Couchbase Sync Gateway

Our main use case requires high performance, scalability, and flexible data storage. For day-to-day use, we use Couchbase Server for JSON-based application data handling, frequently reading and writing data, and also to support some of our workload with low latency.

Our primary use case is for customer data. We use Couchbase Server to store our customer order data, which is in JSON format. On a day-to-day basis, we are using it for fast data querying, fast access, and flexible data storage.

One of the best features is that it is a distributed NoSQL database with high performance and low latency. Additionally, its high availability and horizontal scalability are standout features. Couchbase Server primarily stores data as JSON documents, which is also one of the best features.

The most valuable feature for my team is Couchbase Server's flexible JSON document model in combination with fast data access. It helps my team by making their day-to-day tasks easier because we store related information, such as customer order details, in a single document instead of managing multiple tables and joins. This has reduced development complexity and made changes to the data structure easier to manage. It has also helped us maintain the documentation.

Couchbase Server has had a positive impact on performance, scalability, and development flexibility. It allows us to handle application data with low latency and scale our database as the data volume and workload increase. It also helps us organize the data in a JSON document model, making it easier for team members and developers to access the data. The simpler data management and improved application responsiveness due to its performance have helped us in a positive way.

Overall, Couchbase Server is performing well, but there are a few areas where improvement is needed. First, the configuration and cluster management should be simpler. Second, the monitoring and troubleshooting experience could also be made more straightforward and cleaner. There should also be recommendations when there is a performance or indexing issue.

I have been using Couchbase Server for two years.

Based on scalability, we found that it is very well-suited to handle a large volume of data. We can scale horizontally by adding nodes to the cluster rather than relying on a single large machine. The main benefit is that we can scale incrementally based on demand.

Overall, we have had a positive experience with their customer support. The support team is responsive and helpful, particularly when dealing with issues like performance and configuration.

The first thing I will say is to make the use case clear and also have a total understanding of the learning documents available. I would also recommend Couchbase Server for organizations that need high performance, scalability, flexibility, and low latency. My advice is to first understand your data model and workload. I would rate this product a nine out of ten.

My main use case for Couchbase Server is for frontend application to consume the day-to-day order and retail business order documents.

A quick, specific example of how I use Couchbase Server in my retail business order processing is that customer order workflows have to be synced and reviewed for document expiration. All expired documents with expiry dates and customer orders reside in Couchbase. We have a local database to sync it, and with regular deltas, we sync the sheets or documents from the database. We also use it as a document database to retrieve the details for customer orders.

The best features Couchbase Server offers are fast performance, easy-to-understand queries, and commendable customer support.

The fast performance impacts my work with customer orders because every day I deal with documents and a frontend mobile application. Each time a user opens the application, a sync must occur so that the expiry date is properly synchronized and we do not place a wrong order. The synchronization between systems works very well.

Couchbase Server has positively impacted my organization as we have significantly used it in most of the applications. Given the support, the amount of data we are using, and our specific use case, it is very relevant. It has significantly increased the productivity and quality of our applications.

I am happy with the present support of Couchbase Server, but I would appreciate if customer support were more proactive and provided immediate responses.

I have been using Couchbase Server for roughly five years.

Regarding Couchbase Server's governance and security, I think they are secure.

Regarding the accuracy and reliability of output, I think they are pretty good.

My overall review rating for Couchbase Server is 9 out of 10.

My main use case for Couchbase Server is for integration with iPhone apps, as it has Couchbase Lite that goes on the app, making it very easy to connect to the server side of the equation. It also has the ability to store the database on the phone, allowing the use of the phone without being constantly connected to the server, which is the feature I like the best.

The local storage feature helps my users day-to-day by allowing them to perform necessary database functions, such as invoicing, in situations where they may not have a good connection, as they can preload the database they need in advance.

I usually prototype my setups fairly cheaply so that my clients can use it without worrying about accumulating significant costs.

The best feature Couchbase Server offers is the local database on the iPhone or smartphone, enabling me to conduct analysis or targeting without needing a constant connection to the server.

Using that local database feature allows me to sync data back to the main Couchbase Server very easily. I just need to reconnect to a decent connection and hit a button to run the sync, which confirms whether it has been done, making it a lot easier than SQL server scenarios.

Couchbase Server positively impacts my organization as it serves as a crucial tool for my consulting services, allowing me to offer solutions to my clients' problems.

To improve Couchbase Server, I believe they need more educational tutorials focused on developers, as understanding how to use it effectively is essential, and they should prioritize developer needs over sales perspectives.

I have been using Couchbase Server on and off for about five years.

The versions of Couchbase Server I use are stable, as I primarily test with the open-source version that tends to be more stable than the latest releases.

I have not specifically tested Couchbase Server's scalability, as my clients typically focus on issues related to using a local database on a device and syncing it when reconnected.

Most of my support comes from my own knowledge, as I rarely contact customer support, relying instead on the comprehensive resources available on the website and forums.

I use many different solutions such as MySQL and MariaDB, but I don't use Microsoft SQL because I have a poor opinion of its value, and I view Oracle as particularly unfavorable.

I have seen a return on investment mostly in terms of my clients being able to complete necessary tasks rather than direct monetary savings.

I don't handle pricing, setup costs, and licensing, as my clients do that, and it seems reasonably priced in line with other solutions, especially given my approach limits data transfer costs.

Before choosing Couchbase Server, I evaluated alternatives such as Mongo and other NoSQL databases, but Couchbase Server stood out as the best option, although more developer-focused resources would be beneficial.

Regarding Couchbase Server's AI capabilities, I feel a bit nervous about its governance and security, particularly because some of my clients are in the military, where I prefer to have direct control over programming rather than letting AI operate independently.

Regarding Couchbase Server's AI capabilities, I find its accuracy and reliability of output to be fine, as I haven't had any problems with it.

My advice for others looking into using Couchbase Server is to set up a local server for better performance, especially if a fast network connection isn't available, as this improves development speed and reliability.

I have no additional thoughts about Couchbase Server before we wrap up, other than it makes me want to visit the website to see what new updates might be available, as it's a good software that requires finding the right use case. I would rate this product an 8 out of 10.

We are using RAG here, and Couchbase Server is supported as a vector store, providing a high-performance solution for managing and retrieving vector data. This integration helps us to create a no-code AI workflow with flexible vector storage. Flowise is an open-source tool that allows us to create and manage workflows with a visual no-code interface. We install Flowise using NPM and then use Docker to deploy our project for running it in a container. We upsert into Couchbase Server and run queries using a Couchbase Server retriever. We process and generate data using ChatGPT 4O and handle multi-retrieval using multi-interaction to ensure accurate and context-specific responses.

We have used Couchbase Server in our applications, including a sales plus application. In that application, we moved card validation from the Xamarin code and created a tool to validate all card validations. We directly interact with the document and check each document in Couchbase Server to ensure it is correct. Sometimes we pull data from Couchbase Server, but some attributes are not present. We created a tool to validate the data, and when orders are placed, payment is processed and then shipped, with Couchbase Server storing the state of the active workflow as a JSON document. In our project, we use the same approach. We run a distributed workflow, and if it fails, we can directly fetch the data from Couchbase Server to see how it is handling the data between our microservices without forcing them to communicate directly.

I have used Couchbase Server for approximately four to five years. We interact with Couchbase Server and extract data every day. We created a tool named JSON Copilot tool, which is used for schema conversion and automated validation. Sometimes documents are not proper when we create a table, so we validate the data using this tool. We can directly take the document from Couchbase Server and validate it in our tool. This is very useful for extracting and querying JSON documents without a rigid schema.

We have used a vector approach to get data using the RAG model. It also supports offline data, which is very useful for our team for high-performance applications, caching, and generative AI.

We use Couchbase Server as an eventing service so that the data service can initiate and advance the workflow based on raw data. When we integrated it into the workflow system, Couchbase Server became the center of the data lifecycle. It helps us validate data, and when client applications update a document, Couchbase Server triggers JavaScript-based logic, such as on update and on delete, and instantly forwards that payload to an external orchestrator or message broker. This is very useful for our case.

Couchbase Server's best feature is that it operates directly with no-code workflow builders for modern AI workflows. We support vector store integration, which means we can use an embedded vector knowledge field that can be used by AI to inject semantic search context into the LLM

We could use hyperscale vector indexing and upgrade the storage engine so that density and throughput would be higher. If Couchbase Server introduced natural language query capabilities, that would be helpful for everyone to use it in a more effective manner. The logging part could also be improved.

Advanced security and logging would help us make the application more resilient.

Couchbase Server is the best, but if natural language query capabilities were introduced, it would be easier for developers. Instead of writing queries, they could write using natural conversion language and give prompts to Couchbase Server, and they could get results in seconds.

We have used this solution for approximately five years with AI development.

Couchbase Server is very stable.

Couchbase Server allows our organization to independently scale specific data services horizontally and vertically without downtime.

We have a support team in our project, and they are very helpful. Whenever we encounter an issue, they immediately work to have a solution ready within 30 minutes based on severity if it is a P1 or P2 issue. We have not faced many issues with the Couchbase Server support team. We open ticket escalations, they review them, and then they fix the issue.

We have used Postgres, but we found Couchbase Server to be more scalable and perform better. We have chosen to use this database.

We have evaluated MongoDB and Postgres server. We found Couchbase Server very useful for our application since we are supporting NoSQL documents and validating data. Performance-wise, writing queries is very easy and comfortable in Couchbase Server across distributed applications.

Couchbase Server supports distributed transactions, which ensures data integrity by supporting distributed transactions across the database. It has a decentralized architecture, which prevents single points of failure and enables seamless scaling and disaster recovery.

If you want to adopt Couchbase Server, we should take full advantage of Couchbase Server query language, which allows developers to use familiar SQL syntax to query unstructured and semi-structured JSON data. Leveraging JSON flexibility by designing documents into nested arrays and flattening data together can minimize multi-document joins. This is very useful for using SQL schemas with tables as collections and complex foreign keys. Couchbase Server also has a memory-first architecture that reads data directly from RAM before committing data to disk. It tunes for the active data set. I would recommend every developer to use it. It uses an autonomous operator and tests rebalancing on load. If you want to adopt Capella, there is a team that will fully manage the Capella database as a service to eliminate operational overhead. I would recommend everyone to use this.

Couchbase Server is a high-performance NoSQL document database. We primarily use it for caching and generative AI to develop modern applications using rapid data access and offline support for offline mobile applications, along with flexible data modeling. We have used it for dynamic product catalogs, user profiles, and web applications.

When we started using Couchbase Server, it achieved very good critical business and operational results. The major outcome is consistent microsecond to sub-millisecond response times from read and write operations during periods of concurrent requests. Couchbase Server uses a memory-first architecture, which is very easy for integrating the caching layer. Data is read from and returned to RAM before being persisted to disk.

Couchbase Server has a security architecture that enforces strict data governance and regulatory compliance across distributed edge topology. There is TTL governance that automatically expires sensitive records natively at the document level.

Couchbase Server version 8.8.0 has achieved a balanced combination of operational throughput and data precision up to 93% accuracy while maintaining performance.

Couchbase Server is deployed as a database as a service in a hybrid cloud environment. I would rate this review an 8.