Google BigQuery

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.7 out of 10
N/A
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.
$0
per month
Pricing
Google BigQuery
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Offerings
Pricing Offerings
Google BigQuery
Free Trial
Yes
Free/Freemium Version
Yes
Premium Consulting/Integration Services
No
Entry-level Setup FeeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Google BigQuery
Considered Both Products
Google BigQuery
Chose Google BigQuery
I personally find it by far simpler than Amazon Redshift due it's onboarding seamlessness. For a quick start and simplify tye access to read the data big query provide better user experience and a smoother user interface. More importantly, the fact that Big Query can be easily …
Chose Google BigQuery
Compared to SingleStore, BigQuery has a big advantage of being completely serverless, and without practical limitations.

Compared to RedShift, we found the cost model to be more fitted to our needs.
Chose Google BigQuery
BigQuery can automatically scale to accommodate the data and query load, providing potentially unlimited scalability. At the same time, Redshift requires manual scaling efforts to increase or decrease capacity, which might affect performance during scaling operations.
Chose Google BigQuery
We focused more on data volume and less on full application capabilities. All in all, we found that the two solutions complement each other. For integration, some sources were better handled in SAP HANA, particularly other SAP systems where Google Big Query was more suitable …
Chose Google BigQuery
SingleStore has a much lower query latency compared to BigQuery. Thus, we segregate faster tasks to SingleStore, and use BigQuery has our main database to store all historical data.
Chose Google BigQuery
Google BigQuery is the best among the ones we evaluated. It works really well with the Google Cloud workloads and comes with exceptional security controls. It can be combined easily with lots of products that Google Cloud has. It is a real game-changer.
Chose Google BigQuery
Google BigQuery i would say is better to use than AWS Redshift but not SQL products but this could be due to being more experience in Microsoft and AWS products. It would be really nice if it could use standard SQL server coding rather than having to learn another dialect of …
Chose Google BigQuery
The principal advantage against them and the reason we chose Google BigQuery is the pay-as-you-go service. Every company wants to reduce costs. With this flexible model based on usage, we reduced at least half of the amount we were paying with Azure. Google BigQuery overpasses …
Chose Google BigQuery
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big …
Chose Google BigQuery
Google BigQuery seemlessly integrates with all the Google services. In Looker Studio you directly have a connector for Google BigQuery which can help to create dashboards in few clicks.
For automating some stored procedures we have used Cloud Functions which are triggered by a …
Chose Google BigQuery
Suits well for Business Intellegence and vizualization with Looker. Cloud storage options and seamless integration with Google online products.
Chose Google BigQuery
Google BigQuery is a fully managed, serverless data warehouse offered by Google Cloud Platform. It stands out for its scalability, performance, and ease of use compared to other data warehouse solutions. Here's how it stacks up against others. Google BigQuery is designed to …
Chose Google BigQuery
Cost is the important factor for us compared with all of the other tools Google BigQuery stands top among all of them which charges very minimal charges for storage against all the apps that we have liked the most additionally, we can do query on our data, and can build …
Chose Google BigQuery
I was already familiar with the Google Cloud Platform environment, and I was better equipped with the standard SQL language. Some of the syntax does not translate well to Redshift. It also seemed like many data source integrations relevant to our business were easier and more …
Chose Google BigQuery
I've used Domo while working for an advertising agency and the functionality was way worse and the user interface was not near as good.
Chose Google BigQuery
Google BigQuery is less expensive to run and offers free storage of up to the first 10 GB of data. Google BigQuery is also easier (and faster) to get up and running. Unlike Snowflake, Google BigQuery does not require any manual scaling or performance tuning. Scaling is …
Chose Google BigQuery
Google BigQuery manages data like no one else. The light speed of running queries makes it a one stop solution. The editor and query builder also have a highly intuitive interface that makes it easy to build new queries fast. Google BigQuery can easily be integrated with other …
Chose Google BigQuery
Treasure Data is more for the marketer rather than a developer audience, so depending on who your main users will be for the machine learning you can decide which tool is better. In our case we went with Treasure Data because it was more for a marketer and less for the …
Chose Google BigQuery
BigQuery has a simpler and more intuitive user experience (as is the case with most of its products) compared to AWS, which has a more technical and complex profile, so it was the first tool we used. It's still my go-to option for handling SQL queries, though it doesn't detract …
Chose Google BigQuery
We based our analysis primarily on [BigQuery vs. Redshift vs. Athena] and BigQuery proved to be the best solution for us.
Chose Google BigQuery
At my previous organization we used server based SQL server. There were days when the server was down and we couldn't work or access the data. This caused multiple reports and processes which were fed from the server to fail. Google BigQuery doesn't have such problems.
Chose Google BigQuery
I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. …
Chose Google BigQuery
We selected BigQuery since we were already making use of many other offerings within the Google Cloud Platform and it made sense to stay within that eco-system. Of course, we made sure it met our needs and was cost-effective, and when it did we didn't seriously consider an …
Chose Google BigQuery
Google Cloud BigQuery was our first and last choice for a data warehouse. It serves all of our needs!
Top Pros
Top Cons
Features
Google BigQuery
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.3
50 Ratings
5% below category average
Automatic software patching8.117 Ratings
Database scalability8.350 Ratings
Automated backups8.524 Ratings
Database security provisions8.843 Ratings
Monitoring and metrics8.045 Ratings
Automatic host deployment8.113 Ratings
Best Alternatives
Google BigQuery
Small Businesses
SingleStore
SingleStore
Score 9.7 out of 10
Medium-sized Companies
SingleStore
SingleStore
Score 9.7 out of 10
Enterprises
SingleStore
SingleStore
Score 9.7 out of 10
All AlternativesView all alternatives
User Ratings
Google BigQuery
Likelihood to Recommend
8.5
(50 ratings)
Likelihood to Renew
6.4
(1 ratings)
Usability
9.4
(3 ratings)
Support Rating
10.0
(9 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
Professional Services
8.2
(2 ratings)
User Testimonials
Google BigQuery
Likelihood to Recommend
Google
For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.
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Pros
Google
  • Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
  • Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
  • Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
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Cons
Google
  • Can't use it out of Google's cloud platform which is a minus point if you want a local setup.
  • Can be a little expensive to manage.
  • A little difficult to manage someone with less technical expertise as it requires you to have SQL knowledge of joins, CTEs etc.
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Likelihood to Renew
Google
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.
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Usability
Google
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
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Support Rating
Google
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.
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Alternatives Considered
Google
Google's Firebase isn't a competitor but we had to use Google's BigQuery because Google's Firebase's database is limited compared to Google's BigQuery. Linking your Firebase project to BigQuery lets you access your raw, unsampled event data along with all of your parameters and user properties. Highly recommend connecting the two if you have a mobile app.
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Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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Professional Services
Google
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.
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Return on Investment
Google
  • Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
  • Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
  • Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.
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