Database Engineer at a tech vendor with 1-10 employees
Real User
Top 5
Jul 17, 2026
My use case with CloudBeaver AWS is primarily for one customer. We were using DBeaver, but everyone needs to install that complex, heavy desktop software. The client decided that every employee should have access to a web client, so we adopted CloudBeaver as a cloud solution. However, our observation was that CloudBeaver AWS does not match the performance depth of desktop DBeaver.
I have been using CloudBeaver AWS for around 5 to 6 months. My main use case for CloudBeaver AWS is managing and monitoring multiple databases from a single web interface. As an embedded and IoT focused developer, I mostly use it to check the device logs, validate MQTT related data stored in the database, run SQL queries for debugging, and monitor real-time system data during testing and development. It is especially useful when working remotely because I can access everything through the browser without installing heavy database tools locally. Recently, I used CloudBeaver AWS while testing an IoT fuel station controller system connected through an MQTT and RabbitMQ. One issue we faced was that pump status updates from one device were not reaching the back-end correctly. Using CloudBeaver AWS, I connected directly to the AWS hosted PostgreSQL database and monitored the incoming records in real-time. I ran SQL queries to compare the time when MQTT messages were received from the device, RabbitMQ processed the data, and the final database entry was stored in the system. That helped me quickly identify that the message ID mapping for tank status and pump status was incorrect in the consumer logic. Instead of debugging through logs alone, I could instantly verify whether the live data was getting inserted correctly into the database tables. It saved a lot of time because I did not need separate database client tools or server access. Everything was accessible from the browser itself.
My main use case for CloudBeaver AWS is web-based database access that I can utilize for my entire distributed teams for training and modeling machine learning use cases. For any centralized database management, such as all connections, credentials, and configurations that we need to manage, I can do it perfectly inside CloudBeaver whenever we are using AWS cloud for any model instances or model training on SageMaker. I utilize S3 and EC2 instances for uploading data, but whenever I use CloudBeaver, I can run higher power queries as well, such as whatever it supports in MySQL, PostgreSQL, or MongoDB. All that kind of multi-database support is available inside CloudBeaver AWS. There is easy governance and we can utilize all kinds of local tools as well and easily deployable on EC2 instances or if you want to do it on Kubernetes pods scale then EKS can be utilized as well. Even there are lots of RBAC policies available as well, such as Role-Based Access Control where who can access which databases can be configured and it is very friendly in collaboration. Whenever I utilize my whole use cases for project delivery in my setup of AI architecture or if any data that I want to look out for in AWS RDS, I will jump into CloudBeaver on EC2 and then will look out for the browser. My whole teams or any groups or any collaboration analytics can be identified and then I can have a Python notebook on top of it for model training. Basically I can connect my database to CloudBeaver tool and can perform all kinds of feature engineering via SQL. I can export my whole data for machine learning model training, and I can get the insights as well. That is the main use case I am trying to set up for CloudBeaver tool in AWS for database extraction process. My team collaborates within CloudBeaver AWS by utilizing the collaboration option to work out. In the specific organization scenario, if I am having multiple tables or if I want to join the SQL use cases, then I can make some kind of collaboration and I can connect the database to CloudBeaver, do some feature engineering, and model training will be done. Whenever I want to collaborate with my team, I will identify the role-based accesses for all the features and I can give the permissions as well to that whole database and I can make the tracking as well on top of it of how it is getting utilized, how heavy workflows are integrated, and what kind of training setups are done as well. My accesses can be controlled. My role-based access control can be very smooth in CloudBeaver as well here in AWS and it can be very suitable for any machine learning tasks or any data science-related activities.
DevOps engineer at a tech services company with 51-200 employees
Real User
Top 20
Jan 5, 2026
My main use case for CloudBeaver AWS is connecting to databases on AWS, and I typically use it to make it easy for our employees to connect to the database in the easiest manner.
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...
My use case with CloudBeaver AWS is primarily for one customer. We were using DBeaver, but everyone needs to install that complex, heavy desktop software. The client decided that every employee should have access to a web client, so we adopted CloudBeaver as a cloud solution. However, our observation was that CloudBeaver AWS does not match the performance depth of desktop DBeaver.
I have been using CloudBeaver AWS for around 5 to 6 months. My main use case for CloudBeaver AWS is managing and monitoring multiple databases from a single web interface. As an embedded and IoT focused developer, I mostly use it to check the device logs, validate MQTT related data stored in the database, run SQL queries for debugging, and monitor real-time system data during testing and development. It is especially useful when working remotely because I can access everything through the browser without installing heavy database tools locally. Recently, I used CloudBeaver AWS while testing an IoT fuel station controller system connected through an MQTT and RabbitMQ. One issue we faced was that pump status updates from one device were not reaching the back-end correctly. Using CloudBeaver AWS, I connected directly to the AWS hosted PostgreSQL database and monitored the incoming records in real-time. I ran SQL queries to compare the time when MQTT messages were received from the device, RabbitMQ processed the data, and the final database entry was stored in the system. That helped me quickly identify that the message ID mapping for tank status and pump status was incorrect in the consumer logic. Instead of debugging through logs alone, I could instantly verify whether the live data was getting inserted correctly into the database tables. It saved a lot of time because I did not need separate database client tools or server access. Everything was accessible from the browser itself.
My main use case for CloudBeaver AWS is web-based database access that I can utilize for my entire distributed teams for training and modeling machine learning use cases. For any centralized database management, such as all connections, credentials, and configurations that we need to manage, I can do it perfectly inside CloudBeaver whenever we are using AWS cloud for any model instances or model training on SageMaker. I utilize S3 and EC2 instances for uploading data, but whenever I use CloudBeaver, I can run higher power queries as well, such as whatever it supports in MySQL, PostgreSQL, or MongoDB. All that kind of multi-database support is available inside CloudBeaver AWS. There is easy governance and we can utilize all kinds of local tools as well and easily deployable on EC2 instances or if you want to do it on Kubernetes pods scale then EKS can be utilized as well. Even there are lots of RBAC policies available as well, such as Role-Based Access Control where who can access which databases can be configured and it is very friendly in collaboration. Whenever I utilize my whole use cases for project delivery in my setup of AI architecture or if any data that I want to look out for in AWS RDS, I will jump into CloudBeaver on EC2 and then will look out for the browser. My whole teams or any groups or any collaboration analytics can be identified and then I can have a Python notebook on top of it for model training. Basically I can connect my database to CloudBeaver tool and can perform all kinds of feature engineering via SQL. I can export my whole data for machine learning model training, and I can get the insights as well. That is the main use case I am trying to set up for CloudBeaver tool in AWS for database extraction process. My team collaborates within CloudBeaver AWS by utilizing the collaboration option to work out. In the specific organization scenario, if I am having multiple tables or if I want to join the SQL use cases, then I can make some kind of collaboration and I can connect the database to CloudBeaver, do some feature engineering, and model training will be done. Whenever I want to collaborate with my team, I will identify the role-based accesses for all the features and I can give the permissions as well to that whole database and I can make the tracking as well on top of it of how it is getting utilized, how heavy workflows are integrated, and what kind of training setups are done as well. My accesses can be controlled. My role-based access control can be very smooth in CloudBeaver as well here in AWS and it can be very suitable for any machine learning tasks or any data science-related activities.
My main use case for CloudBeaver AWS is connecting to databases on AWS, and I typically use it to make it easy for our employees to connect to the database in the easiest manner.