Google BigQuery

Google BigQuery

Score 8.8 out of 10
Google BigQuery

Overview

What is Google BigQuery?

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.
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Recent Reviews

BigQuery = Big Win

9 out of 10
April 26, 2021
BigQuery (along with Airflow) has become a critical part of our technology stack. It is being used to support the ingestion of large …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 6 features
  • Database scalability (30)
    9.4
    94%
  • Database security provisions (24)
    9.2
    92%
  • Automated backups (24)
    9.0
    90%
  • Monitoring and metrics (26)
    7.9
    79%

Reviewer Pros & Cons

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Video Reviews

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Pricing

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Queries (Hourly Flex Slots)

$4

Cloud
per 100 slots

Queries (On-Demand)

$5

Cloud
per TB

Queries (Annual Flat Rate)

$1,700

Cloud
per 100 slots

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services
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Features

Database-as-a-Service

Database as a Service (DBaaS) software, sometimes referred to as cloud database software, is the delivery of database services ocer the Internet as a service

8.9Avg 8.7
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Product Details

What is Google BigQuery?

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.

Google BigQuery Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

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.

Reviewers rate Database scalability highest, with a score of 9.4.

The most common users of Google BigQuery are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews

(1-25 of 31)
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Score 10 out of 10
Vetted Review
Verified User
Google BigQuery allows querying multiple datasets within seconds using Sql. It also helps optimise queries to get results quickly.We can preview data without incurring costs. Google BigQuery is a fully managed, serverless, super fast data warehouse with no equivalent in the cloud space. It also creates graph using the data to help generate insights and view trends.
Rohith D | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
I used Google BigQuery for more than two years in my previous roles to retrieve data and build all the necessary pipelines for the different data models that we built across multiple teams. Right from ingesting the data, scoring the data using the model, and sending the output back to the tables, I used Big Query for all these operations.
Lee L Kennedy | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
We use Google BigQuery to pull marketing data into a warehouse, run queries on the data to transform it into a more usable form, and then use the resulting tables as data sources for marketing reporting platforms. The problem it addresses is our need for a comprehensive data warehouse where we can store all our business and marketing data so that we can visualize and report on it later on.
December 19, 2022

Google BigQuery is ok!

Tia Jones | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Google Big Query was a contender for our proprietary database to be used as the cloud database to predict scoring models. We use machine learning to predict if someone is going to default on their loan, and use machine learning to determine how much money someone is eligible for. Google Big Query was an option considered for managing this data.
Score 10 out of 10
Vetted Review
Verified User
We use BigQuery as the company's Data Warehousing tool. The transactional information is handled mostly in Firebase and we inform BigQuery of each update or creation event from which we build the status and history tables. In addition, we use it to consolidate data from other external sources, such as Facebook, Analytics, Google Ads, among others.
Score 10 out of 10
Vetted Review
Verified User
Used to deploy this solutioning to the client by shifting away from traditional data warehouse to cloud data warehouse. It resolves the issue of transparency in terms of payment per month, utilization and on how to allow user level access to different folders. It also allows for full integration with other Google Cloud Platform's components like Compute Engine and PubSub.
Score 10 out of 10
Vetted Review
Verified User
Google Big Query is used by product and services department at my organization. It is used to maintain the various services like bookstore, market place, recreation etc. It is used to maintain the information about inventory, the various vendors and product details. Since it is serverless and can handle large datasets it gives us quick results , work with real time data and helps to handle transactional data.
Score 9 out of 10
Vetted Review
Verified User
As a Data Analyst at my previous company, I dealt primarily with large datasets. Being able to retrieve that data was an important aspect of my job and Google BigQuery made it a lot easier for me. Having worked with the Operations team, I had to use the data in place to find out patterns in them and retrieve a large amount of sales data. At my former organization, BigQuery was used across the organization and it helped a lot to keep all our data at once place with easy access control.
April 26, 2021

BigQuery = Big Win

Score 9 out of 10
Vetted Review
Verified User
BigQuery (along with Airflow) has become a critical part of our technology stack. It is being used to support the ingestion of large amounts of data, manipulating and consolidating that data, and then making it available for other aspects of our technology. The data is at a very large scale and more traditional data stores simply do not have the required performance. For example, some of the same processes if done using a more traditional relational database take hours whereas by utilizing the power of BigQuery take under 1 minute.
Manjeet Singh | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
It is our main data warehouse. It contains raw data and aggregated data. This is also used for aggregation by running scheduled queries on it. With flat pricing, we are able to optimally use it for aggregation, storage, and exploration. BI tools use BigQuery for data exploration.
Cameron Gable | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
BigQuery is used by our Data Science team to do complex queries on large datasets. We have hundreds of terabytes of data and needed a scalable solution that would be able to query our entire biological dataset. BigQuery plays a crucial role in our data lake made up of several Google Cloud data solutions.
Jose F. Gomez | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Google BigQuery is being used as a data warehousing tool so that we can run analytics and calculate business metrics on our data. It’s very comprehensive and powerful yet provides tools to simplify recurrent queries on the data that makes working with it easy enough. We like that it’s a place where multiple people from interdisciplinary teams can converge on the data and work on finding ways to improve and better understand our business.
Score 10 out of 10
Vetted Review
Verified User
Big Query is currently being used by several departments as well as IT to extract data, blend it with other data and to generate reports based on that data. It's being used to track customer journeys through our site, track different channel traffic conversions and to build out dashboards in Tableau.
Tristan Dobbs | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Reseller
Google BigQuery has become our data warehouse for the entirety of the systems that we use. It is incredibly efficient and ridiculously powerful, so it allows us to store all relevant data and query it to build dashboards for management and leadership uses. We connected it directly to a data visualisation tool and it has become the most useful part of our business.
Score 7 out of 10
Vetted Review
Verified User
We use BigQuery to manage large datasets we collect in surveys and in regular work projects. Only one person is in charge of it as we are a small company. It works extremely well for my company because it is cloud-based and we do a lot of remote work, so I can access our data and manage things from anywhere. It's a great tool and makes all kinds of data processing and analysis much easier and faster.
Sam Lepak | TrustRadius Reviewer
Score 5 out of 10
Vetted Review
Verified User
Our marketing team and product development team BigQuery. This is my favorite software for storing information in the cloud, I use it both personally and at work and I recommend it because it has allowed me to access my information very quickly, so far it seems to me that security is very good and not I have had problems with this aspect, although it can work very slow when the Internet connection is not very good, it allows to resume file uploads instead of restarting them every time the signal decreases.
Evan Laird | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
We use Google BigQuery to mine through further data that Firebase doesn't allow us to. It's been extremely scalable and robust for our SQL and backend developers to mine through and get detailed location data on our users so we can find out where our most active cities are in the United States.
Spencer Baselice | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
BigQuery is in use across the entire organization in various departments and businesses for multiple purposes. It is used to store mass data and analytics from web statistics to business data. It is a data warehouse of sorts where different teams are given access to the platform through a central user management base and each team's sandbox contains relevant data to their function.
Score 6 out of 10
Vetted Review
Verified User
In our organization, Google BigQuery is for storing very large data which is created within seconds. We log each and every event done by any user. We also log data like payment status, order status, and user address details. Basically, all of the information is logged. To sort through it we are using BigQuery as it is fast and provides data to us within miliseconds.
Gaurav Gautam | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
BigQuery is a user platform designed to ingest, process and output large volumes of data. We use it to ingest data from Google Analytics and collate that data with other internal data systems by pushing them to BigQuery. BigQuery makes it possible to process a huge volume of data sessions and user levels in almost real-time and achieve personalization and optimized UX.
Score 8 out of 10
Vetted Review
Verified User
We are the reseller of Google Analytics and with Google Analytics premium you get Big Query. You get 500$ credit to use in Big Query. Big Query is a great tool to get unsampled reports, that can be further used for different analysis also to build products on top of it. Big Query can help you to analyze user journey, enhanced eCommerce data for creating remarketing audience. You just need to know SQL and you can use Big Query to get whatever data you want. Big Query can be further utilized for your own purpose, you can upload your CRM data and map with Google Analytics data.
Anatoly Geyfman | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
We use BigQuery as our data warehouse. Meaning, we use BigQuery (BQ) for storing our data, aggregating it and creating pipelines to push data into BQ, and take aggregates out of BQ in order to push them into ElasticSearch. We use it across our whole organization, and most of our data pipelines are now natively using BQ. For us, BQ helped us scale beyond Postgres for very large data sets in a convenient and most importantly, inexpensive way.
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