ScyllaDB Cloud
ScyllaDB, IncExternal reviews
426 reviews
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Scalability and Low Latency Champion
What do you like best about the product?
I really appreciate ScyllaDB's scalability, which is the number one thing I like about it. It allows me to combine multiple events and orchestrate between them seamlessly. ScyllaDB handles low latency well, letting me scale up easily, especially with large training datasets involving 100,000 plus people. It also manages real-time workloads efficiently, helping me coordinate multiple events smoothly.
What do you dislike about the product?
NA
What problems is the product solving and how is that benefiting you?
ScyllaDB provides low latency, allowing me to scale easily and handle real-time workloads efficiently. It supports my conversational AI by retrieving data for chat functionality and orchestrating multiple events.
ScyllaDB Delivers Extreme Performance, Low Latency, and Exceptional Scalability
What do you like best about the product?
ScyllaDB are its extreme performance, low latency, and exceptional scalability. It is widely considered a superior, high-performance alternative to Apache Cassandra, often allowing users to handle larger workloads with fewer nodes.
What do you dislike about the product?
the main drawbacks of ScyllaDB revolve around its steep learning curve, operational complexity, and the specialized, high-resource hardware required to achieve its promised performance
What problems is the product solving and how is that benefiting you?
ScyllaDB solves high-volume data, and, unpredictable,,latency bottlenecks by replacing inefficient Java-based systems (like Cassandra or MongoDB) with a C++ shard-per-core architecture. It provides sub-millisecond, consistent performance at petabyte scale, drastically reducing infrastructure,costs, eliminating manual tuning, and ensuring high,availability
Efficient Data Storage with ScyllaDB's Sharding and Performance
What do you like best about the product?
I appreciate ScyllaDB's sharding and native change data capture, which are pretty significant features that stand out for me. The performance is really a big plus, making it faster for me to get to market. These aspects are crucial, especially when dealing with NoSQL data storage.
What do you dislike about the product?
I dislike the Docker distribution.
What problems is the product solving and how is that benefiting you?
ScyllaDB helps me store NoSQL data efficiently, with features like sharding and native change data capture enhancing performance and speeding up our go-to-market efforts.
Efficient Scaling with Top-Class Compatibility
What do you like best about the product?
I use ScyllaDB for efficient storage. I appreciate its tail latency guarantees and the fact that it scales well beyond my needs, so I don't need to worry about migrating anywhere. I like its compatibility with CQL and the custom extensions that are performance-oriented, such as bypassing the cache for certain queries. I find their Rust driver to be top class for both ScyllaDB and Cassandra, and it's important because latency-sensitive systems are often built in Rust. I also enjoy that the whole ecosystem of tools that works with Cassandra works with ScyllaDB too. The initial setup was easy to follow from the docs.
What do you dislike about the product?
I sometimes wish for more SQL compatibility, like being able to send a complex query that joins multiple tables.
What problems is the product solving and how is that benefiting you?
I trust ScyllaDB's tail latency guarantees and its scalability beyond my needs, so I don't need to worry about migrating. It provides efficient storage.
Predictable Performance, Low Latency at Scale with ScyllaDB
What do you like best about the product?
I appreciate ScyllaDB for its predictable low latency at scale, with P99 latency staying in the single-digit millisecond range even under heavy load. I like that we can use fewer nodes while achieving the same or better throughput, which makes our cluster much smaller. The ability to store both our key-value and vector workloads in one database is a big plus, eliminating the need for a separate vector database for RAG and semantic caching. The CQL compatibility is beneficial as it uses the same query language and driver model as Cassandra, easing both migration and hiring. The Shard-per-core-design is a standout feature—it scales performance predictably with core additions, reducing surprises under load and minimizing the need for constant tuning to avoid hotspots or mysterious latency spikes.
What do you dislike about the product?
Vector search is only on ScyllaDB Cloud right now, and we'd like to see it in open-source/self-managed for on-prem and air-gapped use. Data modeling is strict: partition key design and row/partition size really matter, and it's easy to paint yourself into a corner if you don't plan upfront. There's also a learning curve around compaction strategies, consistency levels, and limits (e.g., tables per keyspaces, partition size) that we had to absorb before we felt confident.
What problems is the product solving and how is that benefiting you?
ScyllaDB provides high write throughput and low latency at scale, allowing us to use fewer nodes with predictable performance. It unifies key-value and vector data needs, eliminating the need for separate databases, and facilitates scalable capacity planning with its shard-per-core design.
Fast and Cost-Efficient for Data Streams
What do you like best about the product?
I use ScyllaDB for educational purposes and I'm impressed with its scalability and cost efficiency. The self-serviced infrastructure is a bonus, and I find the 90% consumption utilization feature very cost-efficient. It's also impressively fast on large data stream real-time analysis. The initial setup was quite easy due to the many available resources.
What do you dislike about the product?
I am generally happy with it. Maybe the time it takes to start the cluster could be faster.
What problems is the product solving and how is that benefiting you?
ScyllaDB is scalable, cost-efficient, with self-service infrastructure. The 90% consumption utilization is economical, and it's very fast for real-time analysis of large data streams. It helps me understand data ingestion technologies better.
Effortless Setup, Maximized CPU Utilization
What do you like best about the product?
I like the rolling upgrades, node replacement, and Grafana integrations with the Scylla exporter, as they make it easy to understand the live workings of the system. I also appreciate that ScyllaDB allows the database to fully utilize the CPU, solving CPU core shared issues. The initial setup of ScyllaDB was so easy.
What do you dislike about the product?
I don't like that the open source is not released after version 5.2.
What problems is the product solving and how is that benefiting you?
ScyllaDB allows our database to fully utilize CPU resources, offering improvements with rolling upgrades, node replacement, and Grafana integration that makes it easy to understand live operations.
High Performance, Low Latency Database Solution
What do you like best about the product?
I like ScyllaDB for its high performance and low latency, even when handling large-scale workloads. The compatibility with Cassandra makes migration and integration easier for us. I also appreciate its efficient resource utilization, allowing us to get better performance with the same hardware, and its strong scalability.
What do you dislike about the product?
One area that could be improved is the learning curve for new users as configuring and tuning can be a bit complex at first.
What problems is the product solving and how is that benefiting you?
I use ScyllaDB to store large volumes of data. It handles high-throughput workloads with low latency and is highly compatible with Cassandra, easing integration. It enhances performance with better resource utilization and provides strong scalability.
Extreme Performance and Efficiency with ScyllaDB
What do you like best about the product?
What I like best about ScyllaDB is its extreme performance and efficiency. It is designed in C++ and uses a shard-per-core architecture, allowing it to fully utilize modern CPUs and deliver very low latency and high throughput compared to many other NoSQL databases.
What do you dislike about the product?
One drawback of ScyllaDB is that it can be complex to deploy and tune properly, especially for beginners, because it requires careful configuration of hardware resources to achieve its best performance.
What problems is the product solving and how is that benefiting you?
ScyllaDB solves problems of high-volume data handling, scalability, and low-latency performance. It benefits me by allowing applications to process large amounts of data quickly and scale efficiently without performance bottlenecks.
High-Performance ScyllaDB for Large-Scale Workloads with Cassandra Compatibility
What do you like best about the product?
ScyllaDB stands out for its performance and efficiency when working with large-scale, data-intensive workloads. The architecture is designed to fully utilize modern hardware, which helps deliver low latency and high throughput even under heavy traffic. I also appreciate that it is compatible with Cassandra, which makes migration or integration easier for teams already familiar with that ecosystem. The documentation and technical talks from the community make it easier to understand distributed database concepts and real-world scaling challenges.
What do you dislike about the product?
Because ScyllaDB is designed for high-scale distributed systems, it can feel complex for beginners who are new to distributed databases. Some concepts around cluster management, tuning performance, and infrastructure requirements may require a learning curve. Smaller teams or projects that do not require extreme scale might find it more advanced than what they need compared to simpler managed databases.
What problems is the product solving and how is that benefiting you?
ScyllaDB addresses the challenge of handling large volumes of real-time data with consistent performance. It is especially useful for applications that require low latency and high availability, such as analytics platforms, messaging systems, and large online services. From a learning and research perspective, exploring ScyllaDB helps deepen my understanding of distributed databases, scalability, and modern data infrastructure, which are important areas as technology and data governance continue to evolve.
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