Couchbase Enterprise Reviews

Couchbase Enterprise is a powerful NoSQL database solution designed for scalability and high performance. I have used Couchbase Enterprise specifically for the performance and querying aspects, where I utilized the N1QL query language, which is the official language for Couchbase. This is a significant advantage because it makes querying documents intuitive for developers who already know SQL.

My main use case includes using the built-in cache of Couchbase Enterprise. The built-in cache layer reduces the need for external caching solutions like Redis, which simplifies architecture. Additionally, it has cross-data center replication so that replication across data centers is smooth and adds resilience if anything goes down. It also integrates well with the ecosystem, including modern frameworks and supports SDKs for multiple languages such as Java.

I used the flexible data model in a real-time billing project where there are situations requiring nested data storage. The nesting can be as deep as five layers, and it is easy to fetch data in the nested layers using Couchbase Enterprise.

Couchbase Enterprise is deployed on-premises in my organization. I used this in Amdocs company, which operates in the telecommunication domain. Couchbase Enterprise is high-performance for real-time workloads and has built-in caching, which reduces the need for external systems. It has cross-data center replication which makes global replication straightforward, and it includes the N1QL query language which is very familiar with SQL-like syntax for querying.

The best features Couchbase Enterprise offers in my experience are performance and scalability because it excels at handling large volumes of data with low latency. The distributed architecture makes it easy to scale horizontally, which is crucial for modern distributed applications. Another valuable feature is the flexible data model because it uses JSON format, which allows me to store complex nested data structures without worrying about rigid schemas. Couchbase Enterprise supports cross-data center replication and integrates well with the Java language. The scalability is impressive as it scales effectively and is excellent at handling large volumes of data with low latency, which is critical for modern applications. Couchbase Enterprise has positively impacted my organization, Amdocs, which has been using it for a very long time.

Couchbase Enterprise can be improved by addressing the complex setup. The initial setup and configuration can be tricky and requires careful tuning of cluster settings, memory quotas, and indexes, which I have already faced.

Another area for improvement is the learning curve. Although N1QL is powerful, there is still a learning curve, especially for developers coming from traditional relational databases.

The licensing cost is something to consider as well. Enterprise licensing can be expensive and may not be ideal for startups or smaller organizations.

There is operational overhead with Couchbase Enterprise, such as managing clusters, monitoring performance, and handling failovers, which require strong DevOps expertise. It would be better if it simplified these aspects and provided better documentation.

I have been using Couchbase Enterprise for three years at Amdocs.

Couchbase Enterprise is highly stable.

The customer support for Couchbase Enterprise is good. I faced an issue in the past, and the customer support helped me with the licensing part and assisted me in setting up Couchbase Enterprise. I would rate the customer support 10 out of 10 as I have not faced any issues.

Positive

In a different project, I used a different solution. I used MySQL in many different projects, but for the NoSQL part, I am using Couchbase Enterprise for the first time.

I have seen a return on investment with Couchbase Enterprise, as fewer employees are needed. Money is saved because if I use any other NoSQL database, it would cost more. Time is also saved because the N1QL query language is very similar to relational databases and traditional databases, so developers do not need much time learning the N1QL query language.

My experience with pricing, setup cost, and licensing is that Amdocs itself pays the pricing and licensing. The setup cost is costly because it takes time to set up and is complex, so it is not very cost-efficient.

I evaluated other options before choosing Couchbase Enterprise, such as MongoDB, but it cost more than Couchbase Enterprise. MongoDB is something different than Couchbase, which is why the team chose Couchbase Enterprise instead of MongoDB.

My advice to others looking into using Couchbase Enterprise is that I highly recommend it based on my experience. I compared Couchbase Enterprise with MongoDB, and regarding the data modeling aspect, Couchbase uses JSON documents with a built-in key-value store, while MongoDB uses BSON-format JSON documents. The query language is N1QL, which is a SQL-query language, but MongoDB uses its own MongoDB query language, which is difficult to learn for anyone coming from a traditional relational database background. For the replication part, Couchbase uses cross-data center replication, which is good, while MongoDB uses replica set sharding. Couchbase has integrated caching, which is very helpful for fetching data, while MongoDB requires external cache solutions like Redis. I would rate this product an 8 out of 10 overall.

I have used Couchbase Enterprise in a different way. I used it in Informatica to set up an end-to-end flow for the connector. Informatica used to connect to Couchbase for all three applications: IICS Cloud and Informatica.

Couchbase is running on a Linux server, then I connected using Informatica connectors and evaluated how the connector works with different bucket sizes. I focused on low latency using high-performance NoSQL stores, data validation, integrating Couchbase with PySpark and Great Expectations. I performed end-to-end API and database testing, including event-driven testing. Mostly, I used it for distributed system testing.

I integrated a workflow as a core data store within a data pipeline for QA validation. Couchbase Enterprise acts as my primary NoSQL database for storing JSON documents such as orders and users. The API interacts directly with Couchbase Enterprise for low latency read and write operations. I validate API responses versus database data consistency and data correctness after business operations. For data pipeline validation, I use PySpark to extract data from Couchbase Enterprise for large-scale validation, which is useful in ETL data engineering workflows. I then use data quality automation with Great Expectations where I perform data quality checks such as schema, null, range, and business rules validation. For end-to-end testing, I verify whether all data and subsequent data landed into the target correctly from source to database. I also tested distributed system scenarios including failover, recovery, rebalancing, replication, and load balancing to ensure the cluster responds correctly without any data loss when a node goes down. I then evaluated query performance across these scenarios.

Couchbase Enterprise offers sub-millisecond response times with built-in memory cache and storage in the storage engine. Rebalancing plus failover are valuable, and the platform supports key-value, multi-model database functionality including key-value support, SQL query, JSON documents, full-text search, and analytics. I can perform relational operations such as joins and aggregations with indexes. Built-in replication, high availability, VBucket system, automatic failover, and cross data replication are all valuable features. There is also mobile edge support and offline sync capability. Enterprise-grade security includes audit logging and compliance with HIPAA and PCI standards. The vector search feature is also a valuable addition.

In my day-to-day work, I mainly use SQL transactions and SQL queries combined with proper indexing because it helps me perform easy validation and fast debugging. Indexing enables strong data validation and increases performance. Support for joins and aggregation helps in defining relationships across the database, and these are the standout features I use.

The best features are high availability, failover, replication, VBucket, and XDCR, which stand out in handling failures without impacting the application. Data is always stored with a replica copy. If a node fails, replica VBuckets are promoted automatically with no data loss and minimal service disruption. This gives me strong confidence and is critical for distributed systems, disaster recovery, and geo-distributed applications. For someone working on data validation and distributed systems, this provides confidence that even under failure conditions, the system maintains data integrity and availability. In addition to SQL++ query capabilities, I really value Couchbase Enterprise's built-in high availability and failover mechanism, the way it handles replication and automatic failover.

For the enterprise, we have faster read and write latency and real-time use cases with fewer bottlenecks. Couchbase Enterprise combined with a database, cache, and query engine helps in faster retrieval of queries and it is a single platform that handles everything. SQL++ query can quickly validate back-end data and debug issues faster. It integrates with PySpark and Great Expectations, so schema validations and data quality rules can be handled much earlier. Built-in failover, replication factor, and failover mechanisms give minimal downtime and high confidence during deployment. Scalability is a major factor as it can scale very easily.

Couchbase Enterprise has significantly improved performance and enabled real-time data access while simplifying our architecture by combining cache and database capability. It has enhanced data validation and testing efficiency through SQL++ query, and its built-in scalability and high availability have allowed us to grow workload reliability with minimal downtime. The cache layer combined with Couchbase Enterprise database cache plus query layer has reduced infrastructure and maintenance cost by twenty to thirty percent with fewer licenses, fewer servers, and less operational overhead. Faster API response due to in-memory architecture and efficient indexing provides better user experience and higher throughput. Reduced debugging time and issue resolution time by forty to fifty percent. PySpark integrated with Great Expectations has improved automation efficiency and reduced manual effort of database checking. Horizontal scaling has improved deployment and scalability speed. From a cost and efficiency perspective, Couchbase Enterprise has helped reduce infrastructure and operational costs and consolidated multiple systems into a single platform. We saw a two to five times improvement in API response and debugging time reduced to nearly five percent. Automation saved about thirty to forty percent in data validation time.

Bucket concepts such as bucket, scope, collection, VBucket are very new to users and take time to understand. Better guided onboarding and simplified documentation with real-world examples could help. Index complexity and management including choosing the right index, managing index fragmentation, and memory overhead could be improved. Smarter index recommendations using AI-driven analysis and better visualizations, data lineage, and understanding of data flow could help users understand how things work. RAM quota, index service memory, and data allocation issues can impact performance and could be solved with more automation of resource optimization. Better cost and performance recommendations can be provided.

Replication lag, failover behavior, and rebalancing issues could benefit from better observability, a more intuitive dashboard, or root cause analysis capability. A dashboard to track licensing and cost would make users aware of their consumption. End-to-end query tracing would be helpful because in real-time projects, creating and dropping indexes through query services and indexing services does not always have obvious performance impacts. Switching between dashboard logs to correlate query latency, index scanning time, and node resource usage takes considerable time.

During scaling or node replacement, rebalancing takes time and system performance can degrade temporarily. More adaptive and throttled rebalance with minimal impact may help. In addition to using Great Expectations, built-in data quality checks within Couchbase Enterprise would help in identifying end-to-end data quality issues. Error reporting and analysis can be improved significantly, which will help in reducing debug time.

I have been using the solution for around six to seven years.

Couchbase Enterprise is stable. This is why we are continuing to work with it and building a connector on top of it. There are no significant issues with Couchbase Enterprise. It is a reliable production environment and a good product.

Horizontal scaling has been very good. Even with multi-dimensional query levels, vertical scaling has been efficient and cost optimization has been achieved.

I would say the setup is moderately easy. Cluster setup, UI, and basic configuration were straightforward. What was challenging was production-level configuration, index planning, AWS integration, and the learning curve for the team in scaling operations.

Organizations that Couchbase Enterprise is best suited for include medium to high e-commerce companies, streaming services, some financial companies, though banking may not be the primary focus. Mobile-first, SaaS, and microservice-based companies are ideal candidates.

I will definitely recommend Couchbase Enterprise to others as it handles high performance, scalability, and real-time data handling effectively. I gave this review a rating of eight out of ten.

For Couchbase Enterprise, I am performing CRUD operations on vendor data, with the primary uses being filtering data, executing CRUD operations, and migrating data from another database.

I had a use case where I searched for important data across all 10,000 vendors to identify which vendor was missing which data. I performed this task twice, once with Couchbase Enterprise and once with MongoDB. When I used Couchbase Enterprise, the utility that I built worked very efficiently with no failures or lags. Conversely, when I did the same with MongoDB, I often faced connection timeouts and unresponsiveness. I am uncertain whether this was an architectural issue or a database-level issue, but we were using the same configurations. I did not face issues in Couchbase Enterprise, but I experienced them in MongoDB. Later, after increasing the timeout limit, things improved in MongoDB.

I have had several positive experiences with Couchbase Enterprise, such as being able to join two buckets or collections effectively. The N1QL language that Couchbase Enterprise supports is significantly better than what MongoDB offers regarding aggregations. While I can perform cross-collection joining in MongoDB, those lookups become quite messy with large amounts of vendor data, but with Couchbase Enterprise, it is much easier. When I perform migrations or search for missing data across approximately 10,000 vendors, the process feels very straightforward because I am using Couchbase Enterprise. The queries are much more readable and helpful, and when I switched to MongoDB, aggregations became more difficult because operations became messy and complex. Using Couchbase Enterprise feels much easier, and in terms of read performance, I find Couchbase Enterprise faster. Comparatively, I did not have to implement caching when using Couchbase Enterprise because results were faster with simple use cases that did not require much calculation, which was a beneficial feature. I also feel Couchbase Enterprise has many configurations available that I did not explore in MongoDB.

In the ecosystem, MongoDB has many communities available for finding solutions, where I think Couchbase Enterprise lacks.

When I faced issues with Couchbase Enterprise, the community support was not strong. I had to invest considerable time and effort to resolve issues. In contrast, when I faced issues with MongoDB, debugging was faster, and the community support was better. Additionally, I worked on some user interface development for a web application with Couchbase Enterprise, but MongoDB provides an intuitive interface with Compass, making it easier to run queries and understand the application's current state. I also heard from a peer about a complex multi-document transaction requiring strict consistency, which Couchbase Enterprise did not support effectively, whereas MongoDB was mature and predictable for such transactions.

I have been using Couchbase Enterprise for a year.

I have worked with Couchbase Enterprise for a year, and then I shifted to MongoDB due to licensing reasons.

I would rate Couchbase Enterprise an eight out of ten.

Regarding this rating, based on my experiences and the use cases I worked on while using Couchbase Enterprise, I felt it was very good. I was able to understand and comfortably perform use cases, whether data migration or standard CRUD operations. In those cases, I found Couchbase Enterprise truly useful compared to the complex aggregations in MongoDB, although both have solid support. I still felt more satisfied with Couchbase Enterprise for the use cases I worked on, though this is not a generalized view.

Pricing, setup cost, and licensing are outside my area of expertise, but I am aware that we moved from Couchbase Enterprise to MongoDB due to licensing issues.

Couchbase Enterprise serves as our primary solution for persisting data into the database, and as a document-based data structure, it is one of the efficient solutions for our architecture.

For example, I had one of the microservices, Product Inventory Sync, and for that, we used particular documents which were first persisted into Couchbase Enterprise; these were Product Inventory Loader documents, and based on ID, it was very easy for me to search and find particular records in our database once they were persisted.

Another scenario related to customer service is the billing account service, where in multiple domains and subdomains we use Couchbase Enterprise, and we chose it for its inbuilt cache, which helped us significantly with performance; additionally, we were able to write queries on top of that and retrieve our data based on customer ID, billing account ID, and various other parameters, allowing easy data searches from Couchbase NoSQL database.

The best features Couchbase Enterprise offers that stand out for me are that we can write SQL queries, search based on ID, and it has inbuilt cache; these features helped us significantly in our e-commerce-related domain with complex structures in the form of JSON data, making it easy to persist complex data structures that include products, features, nested features, characteristics, and external identifiers.

Couchbase Enterprise has positively impacted our organization by reducing latency by 60%; with its inbuilt cache, our performance improved significantly by 50%, and the time taken to search in Couchbase was also reduced, leading to an overall performance improvement of 78.3%. We measured those improvements based on production logs, identifying that a microservice which originally took around six seconds now takes 800 milliseconds, a great optimization noticed by customers, especially during traffic spikes where the response to requests was significantly faster compared to the earlier case of six seconds, using the ELK stack in production along with Kibana and Splunk logs for tracking.

Couchbase Enterprise is a great product overall, however I suggest improvements as there were some instances when retrieving data was slow, especially for a few created buckets on localhost and in production, but these were rare occurrences.

Regarding needed improvements, I would address documentation and integration issues, as it is unclear why Couchbase Enterprise is not as widely discussed as MongoDB despite being a capable NoSQL database, likely due to less clear documentation and somewhat difficult integration processes.

I used it for more than three years.

Couchbase Enterprise is absolutely stable.

Couchbase Enterprise is very efficient in handling growth and increased workloads, proving to be effective during traffic spikes and addressing any problems with horizontal scaling.

Previously, we used an Oracle database, which was based on a monolithic architecture that was difficult to maintain due to complex hierarchy related to e-commerce, including product orders and multiple tables and joins, which is why we switched to Couchbase Enterprise as a great solution.

We have indeed seen a return on investment with Couchbase Enterprise, as it allowed us to save costs by using it for all services, which improved interaction efficiency with microservices and reduced persistence time by 60%, resulting in good profits by providing efficient services to clients.

My experience with pricing, setup cost, and licensing has been fair, and I am satisfied with what the services provide, considering that setup costs and licensing have been reasonable when comparing the services offered.

Before choosing Couchbase Enterprise, we did evaluate other options, including MongoDB, which is widely recognized as one of the market's leading NoSQL databases.

One additional feature to mention is that while working on the PI Sync microservice, we used Elasticsearch alongside Couchbase Enterprise, which provided us with the whole call stack, and in Couchbase Enterprise, the whole document was persisted, which included the complete stack trace for failures, allowing us to easily retrieve those PI Loader documents and persist related events in the database.

My advice for others considering Couchbase Enterprise is that it is easy to deploy and user-friendly; it connects well with microservices locally, making it simple to retrieve and persist documents in production.

I highly recommend Couchbase Enterprise for anyone considering it, especially for use cases involving complex JSON structures, as it is both cost-effective and user-friendly. 

Since I joined the company, I have been using Couchbase Enterprise because the company was experiencing latency issues with SQL databases and wanted to shift to a NoSQL fast persistence solution, which led us to Couchbase Enterprise.

We use Couchbase Enterprise to store user data, as Mobilium is in the telecom domain, and we store telecom-related data such as user profile information, applicable rate plans, PCC rules, and user location information.

I worked in a 4G project related to the Gx interface, where we store session data in Couchbase Enterprise, and we also store the usage done by the subscriber in Couchbase Enterprise.

We also store session data for web services and analytical data in Couchbase Enterprise.

Couchbase Enterprise offers features such as horizontal scalability, providing high availability and performance, and it also includes XDCR replication, which is a great feature.

Based on my experience, the Couchbase Enterprise UI is very helpful for debugging issues, and there is a way to transfer data from one server to another, which is very helpful in development to speed up the development process.

Previously, we used SQL persistent databases, which were not optimized, but since the company shifted to Couchbase Enterprise, the application latency decreased by 70 to 80%, helping the organization attract better clients. Working on a telecom project, we migrated the core logic of the application from SQL persistent database to Couchbase Enterprise, which decreased the application's latency by 70 to 80%, attracting high-profile clients and significantly boosting revenue.

I would appreciate seeing faster index building in Couchbase Enterprise; while enhancements have been made in version 8.0, users frequently look for even faster secondary index builds to reduce bottlenecks during high-volume operations, along with improved rebalance efficiency and continued refinement of the empty node batching technique.

My name is Ishan Thakur, and I work as a software engineer in Mobilium India Private Limited.

Couchbase Enterprise is very stable.

Couchbase Enterprise's scalability is very good, as it supports horizontal scaling, allowing us to add more servers.

Customer support for Couchbase Enterprise is very good.

Positive

Previously, we used an Oracle database and switched due to latency issues.

From my seniors, I have heard that the money needed for the architecture was reduced after using Couchbase Enterprise, though I am not aware of the specific details.

I would rate Couchbase Enterprise an eight.

I gave it an eight because Couchbase Enterprise offers very good features, such as high availability due to its cluster architecture, XDCR replication, and a good UI for debugging issues; plus, it also provides an SDK to interact with Couchbase Enterprise.

If someone is looking for very efficient and fast NoSQL data persistence, then Couchbase Enterprise is the product I would recommend.

Our main use case for Couchbase Enterprise involves feature configuration management in the hospitality domain. Ours is a SaaS product, and we expose our product to multiple customers such as Marriott, IHG, and MGM, which are chains of hotels. We differentiate the features based on their subscription plans, such as one billion subscription plan from GMG versus a 500 million subscription plan from IHG. In this way, we use FCAM to identify and configure the features that specific chains are entitled to use, storing these configurations in separate documents in Couchbase for IHG, MGM, and Marriott.

We use Couchbase Enterprise to store NoSQL data. We have a new feature called feature configuration management where we store each document in Couchbase for each chain, maintaining separate repository configurations for each.

Mostly we use Couchbase Enterprise for this use case only.

Couchbase Enterprise offers very fast retrieval of feature configurations, which is why we choose it over other NoSQL databases available. The downtime is very low, something I have observed over the past two and a half years, making it a notable feature.

I have never experienced any downtime with Couchbase Enterprise even with large volumes of data or complex queries. We have big customers such as Marriott who have around 10,000 to 20,000 features configured under their chain, and we have tried retrieving data multiple times, employing batch jobs and cron jobs without facing any latency.

Couchbase Enterprise positively impacts our organization because we have previously encountered issues with different NoSQL databases in the market such as Aerospike and MongoDB. Our use case involves a lot of transactional calls where latency must be minimal; hence we opted for Couchbase Enterprise to ensure API responses occur in less than 100 milliseconds, which has been very effective for our downstream and upstream components.

Since day one with Couchbase Enterprise, I have never exceeded the TPS configured for each API, with response times typically between 30 to 40 milliseconds.

The UI I am using is not very user-friendly. There are no dropdown options, and I have to manually enter the key and its value to retrieve any specific document, so adding some toggles or dropdowns would be an improvement.

The core product is good, and I only have a concern about the UI, so if they can improve that, it would be great.

I have been using Couchbase since two and a half years in our back end application.

Couchbase Enterprise has not shown any low latency in handling peak loads or high user traffic; its performance remains excellent. Couchbase Enterprise is very stable, and we evaluated various options such as MongoDB and Aerospike before concluding that Couchbase Enterprise was the best fit for our needs.

Couchbase Enterprise is very scalable, and we have a configuration such as KEDA to manage incoming traffic, allowing automatic increases in database instances based on user load, which is effective in handling peak traffic.

We have interacted with Couchbase Enterprise's customer support for troubleshooting issues, and my experience with them was positive; they were very helpful.

I would rate the customer support of Couchbase Enterprise a nine.

Positive

We previously used another NoSQL database that caused many issues, which led us to migrate to Couchbase Enterprise. After the migration, we have not faced any significant issues, and everything has been running smoothly.

Couchbase Enterprise's documentation and community support are useful for basic setup and common issues, although it may not cover all possible problems.

I did not personally handle the integration since I am part of the backend team, but from what I can observe, integrating Couchbase Enterprise was very simple. The libraries provided a lot of basic retrieve queries, allowing straightforward backend integration.

I do not have much insight into Couchbase Enterprise's pricing, setup cost, and licensing. My team typically manages that data, and from my perspective as a user, I just utilize it and deploy our APIs.

The learning curve for new users of Couchbase Enterprise is very easy. We have available documentation; when we did the integration, we documented everything so new team members can easily access these documents to help them understand Couchbase Enterprise.

Couchbase Enterprise is very secure in protecting our data because, in comparison to other NoSQL databases where we have lost data previously, we have not experienced any issues with Couchbase Enterprise.

I would advise others looking into using Couchbase Enterprise to consider it if they want a stable database with low latency and minimal downtime. I would rate this review an eight overall.

My main use case for Couchbase Enterprise is to utilize it for cache, as we have a database as SQL, but we use Couchbase Enterprise for cache.

A specific example of how I use Couchbase Enterprise for caching in my workflow is with two components, ARI and Booking. If ARI is calling the database multiple times, that increases the response time. However, in Couchbase Enterprise, we have a document system that stores a particular response in the form of documents and the key, allowing us to find that document using that key. We just search with that key, and once the key is found, it is readily available for any number of times the request hits, resulting in very good response time once we use the cache.

Couchbase Enterprise also helps us to find and negotiate the time that we use between our calls, and it is very helpful as we can get a visual representation on the UI. We can use a query system to search our documents when needed for searching purposes, and it is a very maintainable thing; it is very light and easy to use.

In my opinion, the best features Couchbase Enterprise offers are the keys and the queries. While all cache providers can provide cache facilities, the key and the document key that gets made, along with the ability to search with a query for the documents that you have, is a very good thing.

The querying capabilities help me in my day-to-day work by being very easy. I can write SQL-like queries. For example, if a document key is ABC, I can search directly as SELECT * FROM ABC.Z.Z, and it will print the whole document or the whole list of data from that. It is very easy to use, user-friendly, and I can customize my queries as I like, making them very specific to my company and my team.

Regarding performance and integration, Couchbase Enterprise performs very well. It is very easy to integrate into components, especially if you are using Java. You just have to integrate a few APIs and dependencies, and the rest depends on the custom integration that you want to do. The extent of customization needed is up to you.

Couchbase Enterprise has positively impacted my organization by significantly reducing the response time; it was nearly cut to half or below that after we introduced Couchbase Enterprise. When we fetch queries from the database, it is very easy. We also introduced a thirty-minute refresh of the documents, keeping them up to date as the data changes every half hour or every ten minutes, making it very helpful with less response time.

I can provide specific metrics on the reduction in response time. When we were using database queries, the user API took around one point five to one point eight seconds to fetch all the data. After using Couchbase Enterprise, it takes nearly zero point five seconds.

Couchbase Enterprise can improve by being a little lighter for use cases as it requires heavy customization. It should be more structured so one can tweak configurations easily. In Couchbase Enterprise, most modifications are necessary, and the boilerplate code is insufficient.

There is room for improvement in documentation regarding integration. Couchbase Enterprise's documentation is good, but for integration purposes, having better boilerplate code would be very helpful for customization and for new users of Couchbase Enterprise.

I have been using Couchbase Enterprise for around two years.

Couchbase Enterprise is very stable in my experience.

Couchbase Enterprise does scale very well. You just have to customize it, and there are many options to pick from. The documents they have are very useful, and you can use the customized variables to manage scalability.

I have interacted with Couchbase Enterprise's customer support team, which was very helpful during a Cloudflare attack that affected our servers. They provided a point of contact for our India team and did everything to resolve our issue, helping us recover lost data effectively.

We did not previously use a different solution; we were looking for caching solutions and comparing Couchbase Enterprise with Redis and others, eventually settling on Couchbase Enterprise.

We purchased Couchbase Enterprise through the Azure Marketplace.

My experience with pricing, setup cost, and licensing was good because we had an agent from our company to assist us with Azure Marketplace, and the process was very smooth.

Before choosing Couchbase Enterprise, we specifically evaluated Couchbase Enterprise and Redis. We chose Couchbase Enterprise because it was price convenient and allowed for more customization in document keys and various adjustments. Redis was more constrained, lacking a UI for visualization, which did not fit our requirements.

We have seen a return on investment because there was significant money saved. We faced many troubles with response times, which means money lost. After starting to use Couchbase Enterprise, we cut the response time to below half, and we observed a twenty to thirty-five percent improvement in pricing, representing overall value for money.

We purchased Couchbase Enterprise through the Azure Marketplace.

My experience with pricing, setup cost, and licensing was good because we had an agent from our company to assist us with Azure Marketplace, and the process was very smooth.

Before choosing Couchbase Enterprise, we specifically evaluated Couchbase Enterprise and Redis. We chose Couchbase Enterprise because it was price convenient and allowed for more customization in document keys and various adjustments. Redis was more constrained, lacking a UI for visualization, which did not fit our requirements.

My advice for others looking into using Couchbase Enterprise is to use it; it is very good and offers a lot of customization options and a better UI than other caches. Once you start using this product, you will see it help decrease response time and increase API capabilities.

For those considering Couchbase Enterprise, I encourage you to try it out for study or evaluation purposes. You will find a proper UI, a solid setup, and comprehensive documentation available online, making it very helpful to get started. Using Couchbase Enterprise will lead to improvements in your applications. I would rate this product an eight out of ten. Thank you.

My main use case for Couchbase Enterprise is the way it gives us low latency and operational analysis, and it also helps us in fraud detection, so these are the main use cases of Couchbase Enterprise. The ability to work with unstructured data and search the unstructured data are additional important aspects.

For a fraud detection example, we are using a JSON-based database where, suppose someone has made a transaction, and we need to track it down.

Couchbase Enterprise helps in the detection of abnormal, suspicious behaviors in a transaction by comparing against expected patterns, allowing us to query based on if the transaction amount is greater than one lakh or if the same card is used within two countries within a short amount of time, such as two minutes or one minute. More short or repeated transactions can help to identify fraud detection.

Talking about the best features of Couchbase Enterprise, I think that the data is served from RAM first and not on the disk, making it low latency and extraordinarily fast for read and write, which is ideal for real-time apps. The model provides key-value, ultra-fast search, and also JSON document and SQL-like query, N1QL, which are very helpful. There is built-in caching and persistence in the data, keeping data in memory while also persisting on the disk for fast querying. The system also has index search and indexing for optimized search, and I feel these features really stand out.

The key-value search is the feature I use most day-to-day, while indexing works in the background. We can query based on the index; together, these two features make it faster. I would also mention the enterprise-grade security, including role-based access control, encryption, and auditing, which stand out as well.

Couchbase Enterprise has positively impacted my organization by making data querying faster, and real-time analytics is also done effectively.

One thing is that better adoption should be there among users, so we can work on this, and a proper learning path should be provided by Couchbase for more adoption.

More documentation should be available easily, and a better connection with other enterprises such as Microsoft Fabric should be established.

I have been using Couchbase Enterprise for more than one year.

Couchbase Enterprise is stable in my experience.

Scaling with Couchbase Enterprise is easy, as it supports horizontal scaling, making it straightforward to scale up or down as our needs change.

Currently, there is no business relationship with Couchbase other than being a customer, but my previous company, which was Silver Technology, had one, although I am currently working in a different organization.

We were previously using other solutions before Couchbase Enterprise.

We manage and monitor Couchbase Enterprise in our environment using the built-in admin console and REST API in Couchbase Server Enterprise Edition for cluster management, and we automate it using CI/CD for development and configuration. For monitoring, we track performance using Couchbase UI metrics.

I see a return on investment with Couchbase Enterprise because the query processing is fast, leading to a financial impact with additional revenue per year for the organization, alongside benefits based on the database and productivity. The productivity of my team has also increased due to the fast processing.

My experience with pricing, setup costs, and licensing for Couchbase Enterprise has been quite good.

We evaluated other options such as SQL Server and MongoDB, and we also considered Cassandra before choosing Couchbase Enterprise.

I have not gotten an opportunity to work on real-time triggers, such as fault alerts and notifications on data pipelines, so these are features I want to explore more in Couchbase Enterprise.

My advice would be to switch to Couchbase Enterprise as early as possible because it is faster, and the ROI is good when investing in Couchbase Enterprise. It has a RAM-heavy design that supports real-time and high-throughput operations. I would rate this review at a 9.

In another product, I use what they call N1QL, which is a query language, and I use it to check if ads are available to show to a user. In one part of the product, I only use the Sync Gateway, and in another part, I use N1QL to query the database.

It's great because it's very different from MySQL where you have to update tables. With document type databases, you just say there's a new element in your document, and that's fine. It's really good to use.

I'm looking into geospatial applications, and I know that Postgres has it more natively than Couchbase. However, I haven't done any testing yet to determine if I should switch databases or if Couchbase is fast enough with geospatial documents.

Once it's set up, it's easy to use, and there are no headaches. I would recommend it. There might be people I wouldn't recommend it to who don't know anything about Linux or how to set up servers. They might need to go a different route, perhaps to the cloud, which is easier to use.

It's a lot to learn and to wire everything up, but that is with every good product, you've got to study. It's similar to studying English to speak it properly.

That was years ago when I had the enterprise plan, and it wasn't really stable then. I think it was around 2018. They did update, and since then, I haven't had any problems anymore.

I haven't used graphs yet. I use key value and document based functionality, and I actually used it 10 years ago. Now it's second nature to me.

I'll continue to explore using Couchbase, particularly looking into geospatial applications. While I know that Postgres has more native support for those uses, I haven't yet tested if Couchbase is fast enough with geospatial documents.

I rate Couchbase a nine out of ten.

Basically we have clusters, Couchbase clusters, databases, and that is how we use Couchbase with XDCR. All the clusters are set up and then we use Couchbase. It is a complex application setup we use with Couchbase. We have replication and multi-node Couchbase setup.

The very useful feature of Couchbase is the online addition and deletion of nodes which we use because of XDCR replication. We use this very regularly.

Couchbase has an indexing part that is easy to build. We can create indexes in advance, before we have the data, and then we can stack in the data. This is something we use regularly.

The main issue we keep facing from the past couple of years, observing other teams using Couchbase, is that whenever there is an upgrade, we see many issues with compatibility or nodes not interconnecting after the upgrade. The upgrade part should be taken care of very carefully.

We contacted Couchbase support recently when we had a similar issue. They were helping, but it is a critical system and application we have set up. They need to rethink about their upgrade process and improve their validation part after the upgrade.

My team has contacted Couchbase support directly. I think they provide good support, but we prefer a proactive approach rather than reactive because this is not the first time we are facing issues after an upgrade. It has happened two or three times. We need to be very cautious during upgrades, and application teams get worried because of earlier experiences. We need to avoid these situations.

Our team has been using Couchbase for a while, but I started using it only in the last year.

We encounter compatibility issues with JDK and similar technologies whenever we perform upgrades. We had some issues recently.

We contacted Couchbase support recently when we had an issue. They were helpful, but since we have a critical system and application, they need to rethink their upgrade process and improve their validation after upgrades.

My team has contacted Couchbase support directly. They provide good support, but we prefer a proactive approach rather than reactive because this is not the first time we are facing issues after an upgrade. It has happened multiple times, requiring extra caution during upgrades, which causes worry among application teams due to previous experiences.

Positive

I worked with other document databases, MongoDB database and Firestore. Each database has its own advantages and disadvantages. When it comes to the deployment part, MongoDB was much easier to deploy, but there are many differences when comparing features.

I worked with other document databases including MongoDB and Firestore. Each database has its own advantages and disadvantages. When it comes to deployment, MongoDB was much easier to deploy, but there are significant feature differences.

I prefer both, but for complex applications requiring minimal downtime, I prefer Couchbase. If there is room for downtime, I would choose MongoDB.

It is a good solution, but as every product needs improvement, this also needs some enhancement. It is a good product. I rate Couchbase seven out of ten.