Google Cloud SQL is a database-as-a-service (DBaaS) with the capability and functionality of MySQL.
$0
per core hour
SingleStore
Score 8.3 out of 10
N/A
SingleStore aims to enable organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads in one unified platform.
$0.69
per hour
Pricing
Google Cloud SQL
SingleStore
Editions & Modules
License - Express
$0
per core hour
License - Web
$0.01134
per core hour
Storage - for backups
$.08
per month per GB
HA Storage - for backups
$.08
per month per GB
Storage - HDD storage capacity
$.09
per month per GB
License - Standard
$0.13
per core hour
Storage - SSD storage capacity
$.17
per month per GB
HA Storage - HDD storage capacity
$.18
per month per GB
HA Storage - SSD storage capacity
$.34
per month per GB
License - Enterprise
$0.47
per core hour
Memory
$5.11
per month per GB
HA Memory
$10.22
per month per GB
vCPUs
$30.15
per month per vCPU
HA vCPUs
$60.30
per month per vCPU
OnDemand
$0.69
per hour
Offerings
Pricing Offerings
Google Cloud SQL
SingleStore
Free Trial
Yes
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
Pricing varies with editions, engine, and settings, including how much storage, memory, and CPU you provision. Cloud SQL offers per-second billing.
Does what it promises well, for instance, as a sidecar for the main enterprise data warehouse. However, I would not recommend using it as the main data warehouse, particularly due to the heavy business logic, as other dedicated tools are more suitable for ensuring scalable operations in terms of change management and multi-developer adjustments.
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.
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.
As with other cloud tools, users must learn a new terminology to navigate the various tools and configurations, and understand Google Cloud's configuration structure to perform even the most basic operations. So the learning curve is quite steep, but after a few months, it gets easier to maintain.
[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.
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.
GCP support in general requires a support agreement. For small organizations like us, this is not affordable or reasonable. It would help if Google had a support mechanism for smaller organizations. It was a steep learning curve for us because this was our first entry into the cloud database world. Better documentation also would have helped.
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
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.
Unlike other products, Google Cloud SQL has very flexible features that allow it to be selected for a free trial account so that the product can be analyzed and tested before purchasing it. Integration capabilities with most of the web services tools are easier regarding Google Cloud SQL with its nature and support.
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.
With managed database system, it has given us near 100% data availability
It has also improved web layer experience with faster processing and authentication using database fields
Google Cloud SQL also gels up well with Google Analytics and other analytics systems for us to join up different data points and process them for deeper dives and analysis
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.