Google BigQuery

Score8.7 out of 10

312 Reviews and Ratings

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.

Categories & Use Cases

Media

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.

1 / 8

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.

Database scalability
Ease of scaling compute or memory resources and storage up or down
Category average: 9.2
Database security provisions
Provision for database encryption, network isolation, and identity access management
Category average: 8.8
Automated backups
Automated backup enabling point-in-time data recovery
Category average: 8

Monitoring and metrics
Built-in monitoring of multiple operational metrics
Category average: 6.8
Automatic software patching
Patches applied to database automatically
Category average: 8.3
Automatic host deployment
Compute instance replacement in the event of hardware failure
Category average: 6.8

Verified User

Engineer in Information Technology (10,001+ employees employees)

Use Cases and Deployment Scope

Analytics Powerhouse. Google BigQuery is the best solution if you want to find trends from your past data. It is a Data warehouse which has SQL and ML capabilities. We have been using Google BigQuery for analyzing our customers billing data and creating dashboards in Looker Studio which can be used by our Sales teams.

Pros

Data Warehousing
Data Analytics
Machine Learning

Cons

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.

Return on Investment

It has really helped us to get insights on out customer spending.
It has improved our customers experience by getting a proper dashboard in a glance.
Google BigQuery is very fast so analyzing Petabytes of data takes minutes. Which is just amazing for company having 100s of customers.

Alternatives Considered

Looker, Looker Studio and Google Cloud Functions

Other Software Used

Looker Studio, Google Cloud Functions, Google App Engine, Google Cloud Datastore

Verified User

C-Level Executive in Information Technology (11-50 employees employees)

Use Cases and Deployment Scope

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.

Pros

Performance at scale.

Cons

Console interface is a little clunky.

Return on Investment

Once up and running, we no longer have to worry about scale and managing infrastructure. Instead of spending our time on our analytics and bringing that value to our customers.

Other Software Used

Google App Engine, Google Cloud SQL, Google Compute Engine

Verified User

Analyst in Product Management (51-200 employees employees)

Use Cases and Deployment Scope

We use Google BigQuery as our central analytical data warehouse to power large-scale event analytics in the form of event tracking and querying, product performance measurement in form of north star metric tracking, and business decision-making. Google BigQuery enables us to store, process, and analyze high-volume, high-velocity event data with low latency and strong reliability

Pros

Large-Scale Event Analytics
Funnel and Conversion Analysis
Product & Platform Metrics

Cons

Query Cost Predictability
Debugging and Error Diagnostics for Complex SQL
Limited Native Support for Funnel and Session Analytics

Return on Investment

it drastically reduced event storage cost
after incorporating Google BigQuery it is easier to loop into looker studio for reporting
ease of access to large data sets

Alternatives Considered

Looker Studio

Francois Salvert View profile

Web Analytics and Dashboarding in Marketing at Blue Cross (201-500 employees employees)

Use Cases and Deployment Scope

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 potential and add other datasets to give business owners a fuller view of their business metrics. BigQuery is also a great addition to either PowerBI or Looker to have more straightforward and more manageable dashboards since BigQuery can handle and centralize the data processing better than end-user tools.

Pros

Cost
Integration with GA4 and Google Ads.
User management and permissions.

Cons

Interface
Some features to schedule queries are a bit annoying to use, and you really need practice to use them well.

Return on Investment

For analytics and dashboarding, BigQuery enables small businesses to achieve the same results as higher priced solution.
Easy to turn on and off. No need for a DBA to do simple tasks.

Alternatives Considered

Microsoft Power BI and Azure SQL Database

Other Software Used

Google Analytics, Looker, Microsoft Power BI

Liz Brandon

Sr Manager Performance Analytics in Information Technology at Beam Suntory (501-1000 employees employees)

Use Cases and Deployment Scope

We use Google BigQuery for all of our key sales and supply chain data sources. We have created a variety of standard certified reporting tables that we connect to either Tableau or PowerBI to build out dashboards to provide our teams with self-service analytics. Most data visualization tools connect seamlessly to data sources in Google BigQuery which makes it very easy to work with the data. We have been using Google BigQuery for over 10 years now. It has allowed us to more easily provide self service data and analytics solutions for various teams within the company.

Pros

Data management
Data connection
Data warehousing
Data access
Data certification

Cons

Provide easier management of security credentials
More seamlessly integrate with Tableau without the constant need to re-authenticate

Return on Investment

Faster access to data
Self service analytics
Increased ROI
Easier data management

Alternatives Considered

Amazon Web Services

Other Software Used

Tableau Desktop, Microsoft Power BI

Usability

Amazon Redshift

Snowflake

Databricks Data Intelligence Platform

Google BigQuery

What is Google BigQuery?

Categories & Use Cases

Media

Key Features

Top Performing Features

Database scalability

Database security provisions

Automated backups

Areas for Improvement

Monitoring and metrics

Automatic software patching

Automatic host deployment

Reviews

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Alternatives Considered

Other Software Used

Usability

Related Products