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Google BigQuery

Score8.6 out of 10

277 Reviews and Ratings

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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

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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.
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.
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.
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.
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.
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.
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.
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.

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Key Features

Learn about the best (and worst!) features Google BigQuery has to offer, as determined by TrustRadius' reviewers.
Based on 277 ratings of Google BigQuery's features
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Top Performing Features

  • Database scalability

    Ease of scaling compute or memory resources and storage up or down

    Category average: 9

  • 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: 8.2

Areas for Improvement

  • Automatic software patching

    Patches applied to database automatically

    Category average: 8.6

  • Monitoring and metrics

    Built-in monitoring of multiple operational metrics

    Category average: 6.7

  • Automatic host deployment

    Compute instance replacement in the event of hardware failure

    Category average: 7.4

Reviews

What users are saying about Google BigQuery.
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BigQuery is a must for GA4 and Google Ads dashboarding!

Incentivized
Francois SalvertView 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

Data Management with Google BigQuery

Incentivized
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

Google BigQuery Usage and Enhancement

Incentivized
Akshay Kulkarni
RPA Developer in Information Technology at AWL Agri Business (1001-5000 employees employees)

Use Cases and Deployment Scope

For Datalack ,Analytics , LLM Training, report generation etc

Pros

  • LLM Training
  • Business Report Generation
  • Automate the business proper for time saving and no manual innervation require

Cons

  • Partitioning for database and split it across multiple cluster
  • Cost Optimized Require

Return on Investment

  • Single Source of truth
  • Easy integrate with any third-party application for inbound and outbound
  • LLM training for building an agentic AI Model

Alternatives Considered

Vertex AI, Google Cloud Storage and Composer

Other Software Used

UiPath Automation Platform, Automation Anywhere, n8n

Scalable Insights For Efficient Integrations Data-Driven Intelligence

Incentivized
Verified User
Executive in Marketing (1001-5000 employees employees)

Use Cases and Deployment Scope

We mostly use Google BigQuery to collect and filter data that's flowing in from multiple streams like GA4 and SFMC which is vital since now we're able to integrate, "clean", and centralise data. The greatest problem it addresses is the accuracy of data; we can run a sql script on Google BigQuery and connect it to our dashboards which we have been doing since adoption. The auto-scheduling option as well is a great feature, as the update runs automatically daily at 11am. My main scope of work is to analyse campaign performance and purchase behaviours on our dashboards, and this is done by the big help of Google BigQuery.

Pros

  • Flattening nested fields for the creation of easy-to-read tabular structures
  • Very efficient integration with all of our Google and CRM tools
  • Integration with Matillion to clean and flatten the data (as per product demonstration)
  • Taking the pressure away of handling infrastructure costs (cost-efficient especially for enterprises like ours that handle very large volume of data)

Cons

  • Mostly how the audiences are created and segmented on Google BigQuery, takes too much time - but this could be a limitation from GA4 side as well (like certain audiences that aren't available on GA4 will need to be built manually)
  • Error messages aren't always accurate when debugging, like "Invalid Operation" - it can be a bit tedious
  • The data in SFMC doesn't always match GA4, occasionally they don't even appear. Figuring all this out can be tricky, especially when we have to track whether it's being exported properly, or if the SQL queries were erroneous.

Return on Investment

  • Campaigns, pages, or tickets that were performing worse than others were identified instantly, leading our brand team to reallocate budgets whenever necessary
  • The speed in which we can obtain results from different tests (like A/B tests) helps our operations team quickly adapt to new strategies, reducing lost revenue opportunities
  • The way in which all the data is consolidated in Google BigQuery means that it becomes our new source of truth, this also saves a lot of time on data reconciliation

Alternatives Considered

Google Analytics and Snowflake

Other Software Used

Salesforce Marketing Cloud, Looker Studio, Microsoft Power BI

Google BigQuery Scalable Cost-Effective Analytics with Room for Governance Multi-Cloud Growth.

Incentivized
Verified User
Manager in Marketing (5001-10,000 employees employees)

Use Cases and Deployment Scope

We have activated the BigQuery export in GA360, and our data flows from GA360 into BigQuery. A Python script has been created to clean the data and store it in a new table within BigQuery. Power BI is connected to BigQuery, where a dashboard has been built. The dashboard updates automatically on a daily basis.

Pros

  • Handling Huge Dataset.
  • Seamless integration with GA.
  • Cost effective.
  • Machine Learning with BigQuery ML.

Cons

  • BigQuery limits the number of concurrent queries per project and sometimes enforces quotas.
  • The BigQuery UI (console) is functional but not as user-friendly as tools like Snowflake.
  • While BQML is great for SQL-friendly ML, it doesn’t cover advanced deep learning.

Return on Investment

  • No infrastructure to manage, pay only for storage and queries.
  • Analysts can run queries on billions of rows instantly without waiting for IT to provision resources.
  • Business users get insights through dashboards (Looker Studio, Power BI) connected to BigQuery.

Alternatives Considered

Snowflake

Other Software Used

Snowflake, AWS Lambda, Anaconda

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