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Google BigQuery

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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Learn from top reviewers

Perfect for Big Data Datawarehousing

Rating: 9 out of 10
April 14, 2025
IncentivizedVetted ReviewVerified User
We use Google BigQuery as the company's data warehouse. We also have some stored queries that levarage the scheduling feature of Google BigQuery. And we use it to connect to s...
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Marcos Vinícius Costa de Carvalho
Analyst - Information Technology
Business Analytics Analist
ACE
Google BigQuery3 years of experience
Verified on LinkedIn

BigQuery is a must for GA4 and Google Ads dashboarding!

Rating: 8 out of 10
February 20, 2025
IncentivizedVetted ReviewVerified User
BigQuery is mainly used to store GA4 data. Google's ecosystem around analytics is becoming solid and well-documented. With BigQuery, we can use GA4 data to its fullest potenti...
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Francois Salvert
Consultant - Marketing
Web Analytics and Dashboarding
Blue Cross
Google BigQuery6 years of experience

Google BigQuery review

Rating: 8 out of 10
February 18, 2025
IncentivizedVetted ReviewVerified User
We use Google BigQuery as a data warehouse to pull data from analytics platforms such as Google Analytics. This allows us to create various tables containing the exact data va...
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Verified user
Analyst
Google BigQuery3 years of experience

Google BigQuery, the big thing

Rating: 7 out of 10
February 6, 2025
IncentivizedVetted ReviewVerified User
We have UI survey reporting database in Google BigQuery. It is meant to give insights of how the users or sales people like the user experience. We recieve files which finall...
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Jeeban Jyoti Das
Team Lead - Engineering
Lead Engineer
Slk
Google BigQuery3 years of experience

Good BigQuery: Speed, Scale, and Simplicity

Rating: 8 out of 10
February 3, 2025
IncentivizedVetted ReviewVerified User
I use Google BigQuery to run codes on Telecom customers to understand their connect and disconnect behavior. Some of the key business problems that I have solved using Google ...
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Umang Aggarwal
Manager - Marketing
Customer Insights Manager
Comcast
Google BigQuery9 years of experience
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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

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Pricing

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Standard edition

$0.04 / slot hour

Cloud

Enterprise edition

$0.06 / slot hour

Cloud

Enterprise Plus edition

$0.10 / slot hour

Cloud

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://cloud.google.com/bigquery/prici…

Offerings

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

Starting price (does not include set up fee)

  • $6.25 per TiB (after the 1st 1 TiB per month, which is free)
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Product Demos

Lesson#6 - BigQuery for beginners| Analyzing data in google bigquery | Step by step tutorial (2020)

YouTube

How to get started with BigQuery

YouTube

BigQuery, IPython, Pandas and R for data science, starring Pearson

YouTube

Google BigQuery Demo

YouTube

Google BigQuery introduction by Jordan Tigani

YouTube
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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

8.4
Avg 8.7
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Product Details

What is Google BigQuery?

Google BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data. At the core of Google’s data cloud, BigQuery can be used to simplify data integration and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make an organization’s operations more data-driven.

BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud.

Store 10 GiB of data and run up to 1 TiB of queries for free per month.


Gemini in BigQuery for an AI-powered assistive experience

BigQuery provides a single, unified workspace that includes a SQL, a notebook and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. Gemini in BigQuery provides AI-powered assistive and collaboration features including code assist, visual data preparation, and intelligent recommendations that help enhance productivity and optimize costs.


Bring multiple engines to a single copy of data

Serverless Apache Spark is available directly in BigQuery. BigQuery Studio lets users write and execute Spark without exporting data or managing infrastructure. BigQuery metastore provides shared runtime metadata for SQL and open source engines for a unified set of security and governance controls across all engines and storage types. By bringing multiple engines, including SQL, Spark and Python, to a single copy of data and metadata, the solution breaks down data silos.


Built-in machine learning

BigQuery ML provides built-in capabilities to create and run ML models for BigQuery data. It offers a broad range of models for predictions, and access to the latest Gemini models to derive insights from all data types and unlock generative AI tasks such as text summarization, text generation, multimodal embeddings, and vector search. It increases the model development speed by directly applying ML to data and eliminating the need to move data from BigQuery.


Built-in data governance

Data governance is built into BigQuery, including full integration of Dataplex capabilities such as a unified metadata catalog, data quality, lineage, and profiling. Customers can use rich AI-driven metadata search and discovery capabilities for assets including dataset schemas, notebooks and reports, public and commercial dataset listings, and more. BigQuery users can also use governance rules to manage policies on BigQuery object tables.

Google BigQuery Features

Database-as-a-Service Features

  • Supported: Database scalability
  • Supported: Database security provisions
  • Supported: Monitoring and metrics

Google BigQuery Screenshots

Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.Screenshot of tracking marketing ROI and performance with data and AI - Unifying marketing and business data sources in BigQuery provides a holistic view of the business, and first-party data can be used to deliver personalized and targeting marketing at scale with ML/AI built-in. Looker Studio or Connected Sheets can share these insights.Screenshot of BigQuery data clean rooms for privacy-centric data sharing - Creates a low-trust environment to collaborate in without copying or moving the underlying data right within BigQuery. This is used to perform privacy-enhancing transformations in BigQuery SQL interfaces and monitor usage to detect privacy threats on shared data.

Google BigQuery Video

Demo: Solving business challenges with an end-to-end analysis in BigQuery

Google BigQuery Competitors

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.

Google BigQuery starts at $6.25.

Snowflake, Amazon Redshift, and Databricks Data Intelligence Platform are common alternatives for Google BigQuery.

Reviewers rate Database scalability highest, with a score of 9.1.

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 From Top Reviewers

View all reviews
(1-5 of 65)

Data Analysis on Steroids with Google Big Query

Rating: 9 out of 10
Incentivized
March 12, 2024
Verified User
Analyst in Information Technology
Internet Company, 11-50 employees
Vetted Review
Verified User
Google BigQuery
1 year of experience
Use Cases and Deployment Scope
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.
Pros
  • Big Query is fast and based on the cloud you can run your query on a huge dataset. Huge means data in TB's. This also reduces the company cost to build that kind of infrastructure to store data.
  • Not specific to Google Analytics but you can import data from different sources for analysis purpose and use the power of the cloud to run the query.
  • Not much time to learn - You don't need any special skills, just SQL and you can use Big Query for your use. Learning SQL is not a big task you can learn it in a week.
  • Big Query refrence schema and different sample query are available to practice on queries.
  • Google also provide sample dataset to use then purchase Big Query.
Cons
  • Though it is SQL some syntax are different but they are getting used to after you use for some time.
  • The legacy SQL is in beta state but can be used and you can run the query with simple SQL.
  • More documentation is needed for using User-defined functions in Big Query.
Likelihood to Recommend
- If you are using Google Analytics and there is huge data that is getting streamed every day then you must have Big Query and use it for analysis. It is not only helpful for analysis but also for debugging your Google Analytics implementations.
- For analyzing a small dataset you don't need Big Query you can use normal MySQL on your own premises. Analyzing on Un-structured data is not possible with Big Query.

Big Query, very powerful in the right context.

Rating: 7 out of 10
Incentivized
March 14, 2024
Verified User
Employee in Corporate
Food & Beverages Company, 1001-5000 employees
Vetted Review
Verified User
Google BigQuery
2 years of experience
Use Cases and Deployment Scope
As a supplement solution to the main enterprise systems for reporting, it is mostly used for the R&D department. The aim was to query rather diverse and semi-structured data from various systems. Some of the sources were wide, some deep and a few were both. Other tools for storing and querying were tried as well.
Pros
  • Good python package.
  • SQL knowledge goes a long way though some peculiars are confusing.
Cons
  • Make it more simple to administrate login from python.
  • Difficult to estimate cost prior.
Likelihood to Recommend
Good for large datasets where query performance is otherwise an issue. It is bad for diverse data sources that are not large enough to really benefit and are overkill. Similar to use cases where many users need to query infrequently, where the minor syntax differences between SQL and Google Big Query can be annoying.

Queries made easy with Google BigQuery

Rating: 9 out of 10
Incentivized
March 12, 2024
IB
Ilyas Bakirov
CTO (Chief Technical Officer)
APD HOLDING (Computer Software, 11-50 employees)
Vetted Review
Verified User
Google BigQuery
2 years of experience
View profile
Use Cases and Deployment Scope
We tried to use Google BigQuery to analyze, perform and build various custom queries to our large set of geological historical data. To solve our needs in geological analysis of huge data, we looked around at what tools would allow us to optimally perform analytical work without capital expensenses and learning new tool.
Pros
  • Managing Data
  • Complex Queries (SQL dialect supported)
  • Integration capabilities with Google products
Cons
  • User interface might be complex for newbies
  • Access management confusing and tight with IAM roles
  • Can be expensive for different workloads types
Likelihood to Recommend
Managed service without any capital investment for users. New users must have knowledge of BigQuery and SQL in order to use it correctly and for its intended purpose. Also scales well and groups according to the size of the dataset and tasks.

Querying TBs of data in seconds without any issues!

Rating: 10 out of 10
Incentivized
March 11, 2020
MS
Manjeet Singh
Principal Engineer
Hike Messenger (Internet, 51-200 employees)
Vetted Review
Verified User
Google BigQuery
3 years of experience
View profile
Use Cases and Deployment Scope
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.
Pros
  • Query performance is awesome.
  • Fully managed.
  • Can be used for all batch jobs or aggregations.
Cons
  • Query pricing is still higher if we don't take flat pricing which is high.
  • Storage pricing is also counted.
  • It do not handle external dependencies.
Likelihood to Recommend
BigQuery is best if we need a fully managed data warehouse with the fastest querying support. The high price may be a point of concern for some.

One stop solution for data management and fast querying

Rating: 10 out of 10
Incentivized
December 21, 2022
YG
Yatin Garg
Product Manager
Cars24 (Automotive, 1001-5000 employees)
Vetted Review
Verified User
Google BigQuery
3 years of experience
Use Cases and Deployment Scope
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.
Pros
  • Automatically optimises queries to fetch data quickly
  • Allows efficient management of data across multiple databases
  • The editor and query builder have a very intuitive interface that makes it easy to build new queries fast
Cons
  • Not able to search specific column fields using search functionality
  • Uploading database using excel is time consuming and error prone
  • The error message thrown while querying can be more customisable to correct the errors
Likelihood to Recommend
Google BigQuery is a great way to manage data across multiple databases within the organisation. The speed of querying makes it highly valuable. The graphs and charts helps analyse the draw insights from the data effectively. We also get a real time understanding of how much time it will take to run the query. We can choose a highly customisable plan as per the need of the organisation to effectively manage the licensing and costs.
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