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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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Recent Reviews
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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 (51)
    8.8
    88%
  • Database security provisions (44)
    8.8
    88%
  • Automated backups (24)
    8.5
    85%
  • Monitoring and metrics (46)
    8.5
    85%

Reviewer Pros & Cons

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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
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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

8.5
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.

Google BigQuery Features

Database-as-a-Service Features

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

Additional Features

  • Supported: Unified experience for all analytics users
  • Supported: Duet AI in BigQuery
  • Supported: Flexibility, predictable pricing, and best price performance
  • Supported: Built-in machine learning
  • Supported: Analyze and share data across clouds
  • Supported: Real-time analytics with streaming data pipelines
  • Supported: Unify, manage, and govern all types of data
  • Supported: Share insights with built-in business intelligence
  • Supported: Geospatial analysis with BigQuery
  • Supported: Real-time change data capture and replication

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 $0.

Snowflake and Amazon Redshift are common alternatives for Google BigQuery.

Reviewers rate Database scalability and Database security provisions highest, with a score of 8.8.

The most common users of Google BigQuery are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(248)

Attribute Ratings

Reviews

(1-25 of 51)
Companies can't remove reviews or game the system. Here's why
Rajender Singh | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Provide real time data for analysis and monitoring purpose.
  • SQL based queries makes it user friendly.
  • It can handle large amount of data.
  • sometime faced performance issues in query execution
  • training material is not easily available
  • Continuous maintenance required
Score 8 out of 10
Vetted Review
Verified User
Incentivized
  • Good place to store historical data.
  • It has free connectors to other Google platforms like Looker, which makes it easy to use as a data source.
  • User interface is easy to navigate.
  • Hard to find data if you don't know where everything is hosted.
  • If you have to upload excel files it takes so long.
  • If you aren't a technical users you likely won't know how to use BigQuery effectively.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
  • Reading and analyzing data
  • Easy access management through GCP
  • Export data easily to further tools such lookers and spreadsheets
  • Query size warning
  • Limitations to daily usage
  • Best practices recommendations
Nir Levy | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Store large amounts of semi-tabular data
  • Allows complex and fast queries
  • Allows streaming of data from different sources
  • Unstructured data is complex to query
  • Costs can be high if using large data sets
  • It's hard to estimate costs as they depend on usage
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Scale automatically to handle datasets of any size.
  • BigQuery can perform extremely fast SQL queries across vast datasets.
  • Pay-as-you-go model, BigQuery allows users to pay only for the data processed and stored.
  • It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
  • The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
  • Syncing with Google products, e.g. Looker studio. Easy to create dashboards when putting a Google BigQuery data table as data source.
  • Scalability. It allows many opportunities across the business.
  • It's easy enough to write SQL statements front-end to explore the data tables.
  • Interface difficult to understand for new users.
  • Not much support provided.
  • Having to wait roughly 24 hours before getting the data from Google Analytics into Google BigQuery. A shorter time would be great.
Deep Mukherji | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • 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.
  • Google BigQuery's built-in visualization tools are limited compared to dedicated BI tools. Expanding the options and allowing for more customization would help explore and present data insights.
  • Currently, it's hard to track where the data comes from and how it changes as it moves through the pipeline because it lacks data lineage capabilities. It's tough to ensure data quality assurance and regulatory compliance.
  • The current access control options are somewhat limited. Granular control over specific datasets or tables within a project would help manage access in collaborative environments.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Data Warehousing
  • Data Analytics
  • Machine Learning
  • The UI and the whole Google BigQuery studio is full of clutter.
  • It's very hard to find error logs related to your application if the backend is Google BigQuery
  • It's hard to share specific tables with someone which has a different place than Cloud IAM.
Ilyas Bakirov | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Managing Data
  • Complex Queries (SQL dialect supported)
  • Integration capabilities with Google products
  • User interface might be complex for newbies
  • Access management confusing and tight with IAM roles
  • Can be expensive for different workloads types
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Store Large data set
  • Very minimal Charge for Storage
  • You can write SQL queries on Google BigQuery
  • There is no training module for Google BigQuery for that reason newcomers will face problems with the user interface and not be familiar with the syntax of SQL query format of Google BigQuery
  • There are some functions which are only used in Google BigQuery which I feel difficult to understand and no one is there for you on how it will work so I think there should be some customer support team would be there where you can raise your concerns with the team.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • Side-by-side view of tabs for easy comparisons
  • Ability to open multiple tabs to switch through different pieces of code
  • Easy to understand layout of projects and tables
  • More detailed descriptions of errors when running code
  • Ability to export larger files as csv
Score 9 out of 10
Vetted Review
Verified User
Incentivized
  • 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.
  • 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.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
  • Allows for fast and efficient analysis of huge amounts of data
  • Allows for running interactive and batch queries
  • Allows for creation of dashboards and reports
  • Allows for real-time analytics on a server-less architecture
  • Streaming data can be expensive
  • Does not support advanced Machine Learning and Deep Learning techniques
  • Number of partitions in tables are limited to 4,000
Score 10 out of 10
Vetted Review
Verified User
Incentivized
  • 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
  • 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
Lee L Kennedy | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
  • Inexpensive data storage.
  • Relatively easy to use interface once you get used to it.
  • Inexpensive query costs.
  • Good number of native integrations.
  • Difficult to use interface if you're not used to it.
  • User management has proven confusing when trying to add new people to projects/accounts.
  • There is no user support, which is a huge issue.
December 19, 2022

Google BigQuery is ok!

Tia Jones | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
  • Cloud based architecture rather than client based architecture
  • There is a free trial
  • Google product so the support is very good
  • Most organizations use SQL so it is a bit of an adjustment
  • No other major issues - serverless data is great and hard to frown upon
  • Large queries run well in the program
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