kdb+ vs. QuasarDB vs. SingleStore

kdb is well suited for real time tick data and time series analytics.

Incentivized

Good for Applications needing instant insights on large, streaming datasets. Applications processing continuous data streams with low latency. When a multi-cloud, high-availability database is required When NOT to Use Small-scale applications with limited budgets Projects that do not require real-time analytics or distributed scaling Teams without experience in distributed databases and HTAP architectures.

Incentivized

• Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
• The database are all file based, very easy to maintain.
• Very solid and fast interface to websocket, so you can interface with javascript easily.

Incentivized

• Technical support is stellar -- far above and beyond anything I've experienced with any other company.
• When we compared SingleStore to other databases two years ago, we found SingleStore performance to be far superior.
• Pipeline data ingestion is exceptionally fast.
• The ability to combine transactional and analytical workloads without compromising performance is very impressive.

Incentivized

• Run time error message readability, particularly for new users.
• Backwards compatibility between versions.

Incentivized

• It does not release a patch to have back porting; it just releases a new version and stops support; it's difficult to keep up to that pace.
• Support engineers lack expertise, but they seem to be improving organically.
• Lacks enterprise CDC capability: Change data capture (CDC) is a process that tracks and records changes made to data in a database and then delivers those changes to other systems in real time.
• For enterprise-level backup & restore capability, we had to implement our model via Velero snapshot backup.

Incentivized

switching costs

Incentivized

We haven't seen a faster relation database. Period. Which is why we are super happy customers and will for sure renew our license.

Incentivized

its ok

Incentivized

[Until it is] supported on AWS ECS containers, I will reserve a higher rating for SingleStore. Right now it works well on EC2 and serves our current purpose, [but] would look forward to seeing SingleStore respond to our urge of feature in a shorter time period with high quality and security.

Incentivized

Solutions are based around a business needs and even when implementing such solution, real time insights are also followed through showing the updates the business are implementing while informing the end users as what is new with technology.

Incentivized

SingleStore excels in real-time analytics and low-latency transactions, making it ideal for operational analytics and mixed workloads. Snowflake shines in batch analytics and data warehousing with strong scalability for large datasets. SingleStore offers faster data ingestion and query execution for real-time use cases, while Snowflake is better for complex analytical queries on historical data.

Incentivized

We don't use it.

Incentivized

The support deep dives into our most complexed queries and bizarre issues that sometimes only we get comparing to other clients. Our special workload (thousands of Kafka pipelines + high concurrency of queries). The response match to the priority of the request, P1 gets immediate return call. Missing features are treated, they become a client request and being added to the roadmap after internal consideration on all client needs and priority. Bugs are patched quite fast, depends on the impact and feasible temporary workarounds. There is no issue that we haven't got a proper answer, resolution or reasoning

Incentivized

Would prefer in person training but for online training, it's almost as good as in person

Incentivized

We allowed 2-3 months for a thorough evaluation. We saw pretty quickly that we were likely to pick SingleStore, so we ported some of our stored procedures to SingleStore in order to take a deeper look. Two SingleStore people worked closely with us to ensure that we did not have any blocking problems. It all went remarkably smoothly.

Incentivized

Python is very commonly used for large data analysis and in general is much easier to pickup than kdb+. The biggest drawback of kdb+ is the learning curve.

Incentivized

Greenplum is good in handling very large amount of data. Concurrency in Greenplum was a major problem. Features available in SingleStore like Pipelines and in memory features are not available in Greenplum. Gemfire was not scaling well like SingleStore. Support of both Greenplum and Gemfire was not good. Product team did not help us much like the ones in SingleStore who helped us getting started on our first cluster very fast.

Incentivized

Very reliable. Coming from mariadb, singlestore has made our application more reliable and faster!

Incentivized

• It perfectly solves most of our real time tick data needs.
• Finding good kdb resources is slightly difficult. Also new people trying to learn kdb experience a relatively longer learning curve.

Incentivized

• As the overall performance and functionality were expanded, we are able to deliver our data much faster than before, which increases the demand for data.
• Metadata is available in the platform by default, like metadata on the pipelines. Also, the information schema has lots of metadata, making it easy to load our assets to the data catalog.