Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Vultr
Score 8.9 out of 10
N/A
Vultr is an independent cloud computing platform on a mission to provide businesses and developers around the world with unrivaled ease of use, price-to-performance, and global reach.
$2.50
per month
Pricing
Google BigQuery
Vultr
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Block Storage
$1
per month
Cloud Compute
$2.50
per month
Object Storage
$5
per month
Kubernetes Engine
$10
per month
Load Balancers
$10
per month
Managed Databases
$15
per month
Optimized Cloud Compute
$28
per month
Cloud GPU
$90
per month
Bare Metal
$120
per month
Offerings
Pricing Offerings
Google BigQuery
Vultr
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Pricing is based on specifications chosen in each product category. Bandwidth is also included up to a certain amount per month.
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
Vultr is amazing for anyone who needs a server and wants to spend time taking care of it, or for someone who wants in on it. WordPress Minecraft kids can use Votr. The scenario is not as appropriate for the people who do not have as much technical knowledge and capacity and don't want to invest in it, not because of Votr itself, but because VPS is not kids' play. Even though we have a Minecraft server and they're quite easy, there will come a time when, if you put your business infrastructure inside Vultr or any VPS, you need to take care of it. Otherwise, it will have a problem.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
There have been times where my VPS is affected by a noisy nieghbour. Would be good to have this actively monitored and mitigated.
Being based in South Africa, it would be nice to have pricing parity options for weaker countries (even if for lower level servers). Sometimes I need throw away or temporary servers, but USD 5 is still expensive in weaker currencies, and can stack up quickly.
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
Just a great product with no bells and whistles, which is the advantage. We spend very little time learning and using Vultr and more time using the systems we have in Vultr to complete our tasks. Not having to worry about the IT overhead is huge and saves a great deal of time
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
easy to use and configure. great bang for the buck. I need an affordable solution to host in the cloud data from systems installed at our client's site with the ability to drill down and change the configuration remotely. Vultr enabled us to do that in an efficient and affordable way.
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
Vultr makes it easy to contact technical support. The techs are very competent. In a number of occasions they have bounced the responsibility back to me when they could have saved us all time and heartache by simply implementing the solution directly
Vultr implementation seemed based on open-source tools and basic cloud principles - some things were more complicated to do compared with more developed cloud providers, but on the other hand it was more extensible by open-source tools.
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
Linode is a more old-school offering. Linode pricing model and infrastructure rely on classic Virtual Machines. What we like about Vultr is that they offer the same at the front, but in the back, the machines are much more flexible and can be tailor-made to our needs, which of course also impacts the costs of running the infrastructure.
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
