I like ScyllaDB's ability to handle high throughput workloads while maintaining consistently low latency. The horizontal scalability makes it easy to accommodate increasing data volumes and traffic without major performance degradation. I appreciate ScyllaDB's low latency performance, which remains consistent even when workload and traffic increase. The shard per core architecture is a big advantage because it minimizes resource contention and allows the database to make efficient use of available CPU, memory, and storage. Additionally, I find the initial setup straightforward, as we used Docker Compose to set up a 3-node cluster across different physical nodes in the same network. ScyllaDB integrates well with our existing technology stack, helping us manage high-volume data workloads without significant changes to the application architecture. Review collected by and hosted on G2.com.

I find the learning curve to be challenging, especially around data modeling, partitioning, and cluster tuning. For new teams, it can be tough to understand data modeling and partitioning best practices and how to design tables around application access patterns. I think more practical examples, guided tutorials, and real-world production scenarios would make this easier. Review collected by and hosted on G2.com.