Database Engineer at a tech vendor with 1-10 employees
Real User
Top 5
Jul 17, 2026
CloudBeaver's web interface frequently slows down or freezes when handling complex workloads, such as large PL/pgSQL statements or complex Common Table Expressions (CTEs) exceeding 500 lines. The screen freezes up, likely due to how the application manages the browser's in-memory capabilities. Having to constantly refresh the page is a major pain point. They desperately need to implement virtualized scrolling and background pagination so the UI doesn't hitch while queries run. From a security standpoint, the administration controls for managing database connection scopes should be more granular. Right now, providing a connection often opens up visibility to all tables, views, and procedures by default. It needs to be much easier for an administrator to mask specific columns or restrict connection contexts to designated tables. Additionally, the ER diagram visual tools are not nearly as intuitive or detailed as they are in the desktop version of DBeaver. Lastly, we experienced frequent connection drops when executing longer running procedures or monitoring active database jobs.
CloudBeaver AWS already covers most core database management needs very well, but a few improvements could make it even better for teams working with real-time systems and cloud monitoring. One thing I would like to see is a more advanced real-time monitoring dashboard built directly into the platform. Right now, it is great for querying and checking live data, but having customizable live widgets for alert panels for database active, failed queries, or IoT event streams would be really useful. From the UI side, the interface is clean already, but advanced filtering and dashboard customization options could improve the experience further for enterprise-scale monitoring environments.
CloudBeaver AWS can be improved because in rendering of the queries, if it is very complex or big, the responses in the browser get slowed down. Compared to DBeaver of desktop, it is noticeably very slower on the browser of AWS and heavy data engineering can be done, but it will have very slow responses configured altogether. That needs to be maintained. Even there is no connectivity of machine learning, MLflow kind of thing where Airflow or PySpark approaches can be integrated. Python pipelines can be created but the whole end-to-end machine learning pipeline gets stuck whenever we work out with DBeaver. That again is one of the issues that I would look out for to improve. Also, I need to maintain the infrastructure perfectly here. I need to manage it and need to identify the risks as well. The whole proper setup of VPCs or IAMs needs to be done. It is not a NLQ kind of thing. User queries need to be configured in manual approaches, not automated currently. It should be automated now. Debugging is very painful. That again is a vague approach here. Errors can be executed and we will not be getting out the clarity as well. During this whole approach, the logs are not perfect and intuitive and debugging is also very limited. The user interface and documentation look good, but I would still suggest improvements.
DevOps engineer at a tech services company with 51-200 employees
Real User
Top 20
Jan 5, 2026
While CloudBeaver AWS meets most of our needs, there are a few areas where it could be improved. Enhanced support for more advanced query visualization and analytics features would be useful for our data teams. Performance optimization for very large datasets could also help. More granular auditing and alerting features would strengthen the security perspective. In terms of security, I can improve more of the features of CloudBeaver AWS. I could bring in encryption functionality and also store the database credentials in the AWS Secret Manager to integrate it with more AWS services. An additional area that could be improved is the user interface for managing multiple databases, including a more streamlined interface for managing multiple connections, better handling of long-running queries, and deeper integration with AWS services and other services to support larger teams with complex workflows. Instead of manually installing CloudBeaver AWS, I could directly attach it to the CI/CD pipeline. That would increase productivity. I could easily and directly connect the database endpoint to CloudBeaver AWS, which would be a more time-saving method, rather than manually going to the CloudBeaver AWS console and entering each database endpoint, username, password, and database name. In addition, it would be valuable if I could combine the AWS SSM Manager with CloudBeaver AWS to connect easily using localhost. That would be beneficial for configuring a bastion host. Some environments, companies, or organizations prefer using a bastion host over a VPN, so for them, that would be a great addition.
CloudBeaver AWS offers a robust open-source database management solution tailored for AWS environments. It's designed to optimize database operations with user-friendly features, making it ideal for developers and database administrators seeking efficient management.Designed for seamless integration, CloudBeaver AWS enhances database workflows by providing intuitive management tools and a collaborative platform. Its architecture supports multi-cloud environments, offering flexibility for...
CloudBeaver's web interface frequently slows down or freezes when handling complex workloads, such as large PL/pgSQL statements or complex Common Table Expressions (CTEs) exceeding 500 lines. The screen freezes up, likely due to how the application manages the browser's in-memory capabilities. Having to constantly refresh the page is a major pain point. They desperately need to implement virtualized scrolling and background pagination so the UI doesn't hitch while queries run. From a security standpoint, the administration controls for managing database connection scopes should be more granular. Right now, providing a connection often opens up visibility to all tables, views, and procedures by default. It needs to be much easier for an administrator to mask specific columns or restrict connection contexts to designated tables. Additionally, the ER diagram visual tools are not nearly as intuitive or detailed as they are in the desktop version of DBeaver. Lastly, we experienced frequent connection drops when executing longer running procedures or monitoring active database jobs.
CloudBeaver AWS already covers most core database management needs very well, but a few improvements could make it even better for teams working with real-time systems and cloud monitoring. One thing I would like to see is a more advanced real-time monitoring dashboard built directly into the platform. Right now, it is great for querying and checking live data, but having customizable live widgets for alert panels for database active, failed queries, or IoT event streams would be really useful. From the UI side, the interface is clean already, but advanced filtering and dashboard customization options could improve the experience further for enterprise-scale monitoring environments.
CloudBeaver AWS can be improved because in rendering of the queries, if it is very complex or big, the responses in the browser get slowed down. Compared to DBeaver of desktop, it is noticeably very slower on the browser of AWS and heavy data engineering can be done, but it will have very slow responses configured altogether. That needs to be maintained. Even there is no connectivity of machine learning, MLflow kind of thing where Airflow or PySpark approaches can be integrated. Python pipelines can be created but the whole end-to-end machine learning pipeline gets stuck whenever we work out with DBeaver. That again is one of the issues that I would look out for to improve. Also, I need to maintain the infrastructure perfectly here. I need to manage it and need to identify the risks as well. The whole proper setup of VPCs or IAMs needs to be done. It is not a NLQ kind of thing. User queries need to be configured in manual approaches, not automated currently. It should be automated now. Debugging is very painful. That again is a vague approach here. Errors can be executed and we will not be getting out the clarity as well. During this whole approach, the logs are not perfect and intuitive and debugging is also very limited. The user interface and documentation look good, but I would still suggest improvements.
While CloudBeaver AWS meets most of our needs, there are a few areas where it could be improved. Enhanced support for more advanced query visualization and analytics features would be useful for our data teams. Performance optimization for very large datasets could also help. More granular auditing and alerting features would strengthen the security perspective. In terms of security, I can improve more of the features of CloudBeaver AWS. I could bring in encryption functionality and also store the database credentials in the AWS Secret Manager to integrate it with more AWS services. An additional area that could be improved is the user interface for managing multiple databases, including a more streamlined interface for managing multiple connections, better handling of long-running queries, and deeper integration with AWS services and other services to support larger teams with complex workflows. Instead of manually installing CloudBeaver AWS, I could directly attach it to the CI/CD pipeline. That would increase productivity. I could easily and directly connect the database endpoint to CloudBeaver AWS, which would be a more time-saving method, rather than manually going to the CloudBeaver AWS console and entering each database endpoint, username, password, and database name. In addition, it would be valuable if I could combine the AWS SSM Manager with CloudBeaver AWS to connect easily using localhost. That would be beneficial for configuring a bastion host. Some environments, companies, or organizations prefer using a bastion host over a VPN, so for them, that would be a great addition.