Data Analyst at a university with 5,001-10,000 employees
Real User
Top 10
Feb 17, 2026
I used CrateDB for a one-off project from April to August in 2025. My main use case was a digital twin project designed to create a FIWARE-based application with a focus on optimizing the data management and repository component. I used tools including Orion Context Broker, MongoDB, QuantumLeap, and configured CrateDB for advanced query and large-scale analytics. I ended up using Grafana for data visualization and monitoring with an interactive dashboard. CrateDB performed excellently in handling large-scale analytics and integrating with other tools such as Grafana and Orion Context Broker. I was able to make some really nice analysis with CrateDB. Using Grafana for visualization was a scale-up approach because we needed the free version, and it was very beneficial seeing visualizations on Grafana while doing database analytics and SQL operations on CrateDB.
DevOps Cloud and Data Senior Consultant at a outsourcing company with 51-200 employees
Real User
Top 10
Jan 5, 2026
At my previous company, which was a security analytics tool, my main use case for CrateDB was to ingest any kind of logs from email, applications, firewalls, DNS, Microsoft, and other technology tools. We used to ingest logs, which are small text files with information of what occurred, and after a process of going through a Kafka queue, they would be stored in CrateDB as a long-term storage option. Later, we would retrieve this data to look for anomalies in a UI-based platform. CrateDB fit well into our pipeline and use cases in general terms. In some situations where the customer was very big or the data volume was huge, there might be a little delay because we were using CrateDB installed by us in AWS servers. Sometimes those servers were not powerful enough, so there was some delay, but after restarting it, all worked pretty well and I think it was a great solution for our use case. CrateDB positively impacted my organization by reducing the time needed for processes. Sometimes with other data tools, like Snowflake, it would take a long time to store and retrieve all the logs quickly. Its scalability was also impressive, as it was easy to start with one server and then horizontally scale to multiple nodes to retrieve data. These aspects stood out for our use case and helped my company gain more customers during my time there.
CrateDB is a highly scalable SQL database designed for real-time analytics on machine data, capable of handling structured and unstructured data with ease.CrateDB provides a unique approach to database management by combining SQL accessibility with NoSQL flexibility. It offers horizontal scaling and distributed SQL queries, enabling users to perform time-series data analysis and geospatial queries effectively. Known for its ability to handle large data volumes, CrateDB is ideal for industries...
I used CrateDB for a one-off project from April to August in 2025. My main use case was a digital twin project designed to create a FIWARE-based application with a focus on optimizing the data management and repository component. I used tools including Orion Context Broker, MongoDB, QuantumLeap, and configured CrateDB for advanced query and large-scale analytics. I ended up using Grafana for data visualization and monitoring with an interactive dashboard. CrateDB performed excellently in handling large-scale analytics and integrating with other tools such as Grafana and Orion Context Broker. I was able to make some really nice analysis with CrateDB. Using Grafana for visualization was a scale-up approach because we needed the free version, and it was very beneficial seeing visualizations on Grafana while doing database analytics and SQL operations on CrateDB.
At my previous company, which was a security analytics tool, my main use case for CrateDB was to ingest any kind of logs from email, applications, firewalls, DNS, Microsoft, and other technology tools. We used to ingest logs, which are small text files with information of what occurred, and after a process of going through a Kafka queue, they would be stored in CrateDB as a long-term storage option. Later, we would retrieve this data to look for anomalies in a UI-based platform. CrateDB fit well into our pipeline and use cases in general terms. In some situations where the customer was very big or the data volume was huge, there might be a little delay because we were using CrateDB installed by us in AWS servers. Sometimes those servers were not powerful enough, so there was some delay, but after restarting it, all worked pretty well and I think it was a great solution for our use case. CrateDB positively impacted my organization by reducing the time needed for processes. Sometimes with other data tools, like Snowflake, it would take a long time to store and retrieve all the logs quickly. Its scalability was also impressive, as it was easy to start with one server and then horizontally scale to multiple nodes to retrieve data. These aspects stood out for our use case and helped my company gain more customers during my time there.