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Google BigQuery

Score8.8 out of 10

310 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

Media

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 310 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: 9.2

  • 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

Areas for Improvement

  • 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

Reviews

What users are saying about Google BigQuery.
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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

Analytics Powerhouse with Advanced Machine Learning features.

Incentivized
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

BigQuery = Big Win

Incentivized
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

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

For best value for your buck go for Google BigQuery

Incentivized
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

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