ScyllaDB Enterprise

What do you like best about the product?

It delivers strong write-heavy performance with low latency. The main use cases for me are IoT and gaming. I also value its CQL compatibility, along with horizontal scalability and high availability.

What do you dislike about the product?

ScyllaDB lacks a native, polished management dashboard out-of-the-box comparable to some competitors. some niche integrations or modern cloud-native tools may not have first-party support or optimized drivers for ScyllaDB specifically, requiring extra configuration.

What problems is the product solving and how is that benefiting you?

ScyllaDB addresses high latency, scalability, and throughput challenges in distributed databases. It delivers faster query performance, more efficient resource utilization, and smooth horizontal scaling. For me, this translates into better application reliability, support for growing workloads, fewer operational bottlenecks, and more predictable performance during traffic spikes, while also simplifying overall database infrastructure management.

What do you like best about the product?

I use ScyllaDB as the session store for my agent runtime, and it handles write-intensive traffic really well, which is essential for our agent swarm service as we need to write to the session store as the agent progresses. I like the strong availability ScyllaDB offers. Additionally, the natural partitioning based on the key aligns perfectly with my service's session ID and client's namespace as partition keys, which helps a lot. The initial setup on ScyllaDB Cloud was pretty easy.

What do you dislike about the product?

I wish the self-deployment could be easier. Having a one-click deployment feature on Google Cloud would be perfect.

What problems is the product solving and how is that benefiting you?

I use ScyllaDB for handling write-intensive traffic due to its strong availability and natural key-based partitioning. We switched from Postgres to ScyllaDB as our write volume grew.

What do you like best about the product?

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.

What do you dislike about the product?

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.

What problems is the product solving and how is that benefiting you?

We use ScyllaDB to handle high-volume data ingestion and low-latency access with horizontal scalability. It's great for real-time events and time-series data, and it manages high throughput while maintaining consistent low latency, efficiently using CPU, memory, and storage.

What do you like best about the product?

I like how ScyllaDB makes large-scale distributed data feel simple and allows me to work with its design patterns effortlessly. I really appreciate the automatic distribution and the hot reload performance of 1 million operations per second. One thing I especially like about ScyllaDB is its high performance and predictable low latency, even under heavy workloads. I also value its compatibility with the Cassandra ecosystem, which makes migration and integration straightforward. Features like shard-aware drivers, horizontal scalability, and efficient resource utilization work particularly well for large-scale, high-throughput applications like ours.

What do you dislike about the product?

Overall, my experience with ScyllaDB has been positive, but there are a few areas that could be improved. Initial cluster setup and tuning can be complex, especially for production deployments. Troubleshooting shard-awareness and networking issues also required a good understanding of the architecture. I also think the documentation could include more end-to-end examples and performance tuning guides for large-scale deployments. I would like to see more built-in observability and performance diagnostics. Additionally, although I use ScyllaDB with GUI tools like DataGrip and DBeaver for querying data, browsing tables, and schema management, I feel these tools are often paid and limit users for a better experience interacting with ScyllaDB.

What problems is the product solving and how is that benefiting you?

I use ScyllaDB for managing large-scale distributed storage, automatic distribution, and high throughput reloads without cluster degradation. It handles even data distribution, sub-millisecond latencies on read-heavy workloads, and offers compatibility with Cassandra for easy migration, enhancing our scalability and resource utilization.

What do you like best about the product?

ScyllaDB provides exceptional throughput and sub-millisecond, predictable latency. Because it is written in C++ rather than Java, we completely eliminated garbage collection pause issues and saw a massive performance boost over Cassandra with far fewer nodes.

What do you dislike about the product?

ScyllaDB delivers high performance, but its ecosystem of third-party monitoring plugins and integrations is still a bit smaller than what you’ll find with older databases such as Apache Cassandra.

What problems is the product solving and how is that benefiting you?

Before we started using ScyllaDB for our distributed systems coursework and hackathon prototypes, we often ran into severe latency spikes and unpredictable Garbage Collection (GC) pauses with traditional Java-based NoSQL databases such as Cassandra, especially under high concurrent loads. ScyllaDB removed that bottleneck by leveraging its C++ architecture along with a shared-nothing, shard-per-core design.

By avoiding JVM overhead, our applications can keep sub-millisecond, predictable latency even when traffic suddenly spikes. And as students working with minimal cloud resources and strict hardware budgets, we’ve found that ScyllaDB delivers much higher throughput on a significantly smaller footprint, helping us maximize single-node CPU and RAM utilization without needing expensive multi-node clusters.