Google BigQuery Reviews & Ratings 2025

Name: Google Cloud Data Analytics in 10 minutes
Uploaded: 2023-02-03T17:26:43.000Z
Duration: 10 min 41 s
Description: Demo: Solving business challenges with an end-to-end analysis in BigQuery

Starting at $6.25 per TiB (after the 1st 1 TiB per month, which is free)

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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.

Seamless and near real-time integration for GCP users.

Rating: 8 out of 10

September 21, 2024

IncentivizedVetted ReviewVerified User

BigQuery fits naturally into the GCP estate, seamlessly integrating with GSheets and allowing users to ingest data into BigQuery with little to no technical knowledge. This is...

Verified user

Engineer

7 years of experience

Honest review about G bigQuery.

Rating: 10 out of 10

September 21, 2024

IncentivizedVetted ReviewVerified User

We use BigQuery to analyze marketing campaign performance, track customer engagement, and optimize marketing strategies for over 40 clients. This involves processing large vol...

Wadie Aroua

Administrator - Marketing

Directeur technique et Co-fondateur

Advance Web

10 years of experience

BigQuery is a must for GA4 and Google Ads dashboarding!

Rating: 10 out of 10

September 21, 2024

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...

Francois Salvert

Consultant - Marketing

Web Analytics and Dashboarding

Blue Cross

6 years of experience

Google BigQuery, for all your big ideas.

Rating: 9 out of 10

August 28, 2024

IncentivizedVetted ReviewVerified User

I think Google BigQuery is the perfect repository for all of our cleansed data to be used for analytics and determining trends. It's the perfect system to hook up to using a n...

Adam Rooney

Employee - Information Technology

Senior Developer

M and M Direct

6 years of experience

Google BigQuery for your Data Manipulation needs

Rating: 10 out of 10

August 20, 2024

IncentivizedVetted ReviewVerified User

Google BigQuery helps us solve business questions, specially ad-hoc ones, it allows us to search specific information through enormous amounts of data, all that while reducing...

Avelino Munoz

Professional - Customer Service

Enforcement specialist

Etsy

2 years of experience

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

View all pricing

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

8
Automatic software patching
(17) Ratings
Patches applied to database automatically
9
Database scalability
(67) Ratings
Ease of scaling compute or memory resources and storage up or down
8.5
Automated backups
(24) Ratings
Automated backup enabling point-in-time data recovery
8.8
Database security provisions
(61) Ratings
Provision for database encryption, network isolation, and identity access management
8.2
Monitoring and metrics
(63) Ratings
Built-in monitoring of multiple operational metrics
8
Automatic host deployment
(13) Ratings
Compute instance replacement in the event of hardware failure

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Product Details

About
Competitors
Tech Details
FAQs

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

Show Unsupported 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 Types	Software as a Service (SaaS), Cloud, or Web-Based
Operating Systems	Unspecified
Mobile Application	No

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.

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

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