What is Google BigQuery?
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. Use BigQuery to manage all data types across clouds, structured and unstructured, with fine-grained access controls. BigQuery's serverless architecture lets you use SQL queries to analyze your data. You can store and analyze your data within BigQuery or use BigQuery to assess your data where it lives.
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Real user data aggregated to summarize the product performance and customer experience.
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Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
88 Likeliness to Recommend
97 Plan to Renew
86 Satisfaction of Cost Relative to Value
1
Since last award
Emotional Footprint Overview
Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
+91 Net Emotional Footprint
The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.
How much do users love Google BigQuery?
Pros
- Respectful
- Enables Productivity
- Acts with Integrity
- Security Protects
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
Negative
Neutral
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Feature Ratings
Data Integration
Distributed Processing
Data Management
Data Security
Analytics and Data Science Tools
Real Time Capabilities
Analytics and Reporting
Platform Administration
Metadata Management
Workload Management and Monitoring
Data Visualization
Vendor Capability Ratings
Business Value Created
Ease of Implementation
Usability and Intuitiveness
Quality of Features
Ease of Data Integration
Product Strategy and Rate of Improvement
Breadth of Features
Ease of IT Administration
Vendor Support
Availability and Quality of Training
Ease of Customization
Google BigQuery Reviews
Eyad B.
- Role: Sales Marketing
- Industry: Transportation
- Involvement: Business Leader or Manager
Submitted Feb 2024
Likeliness to Recommend
Pros
- Reliable
- Enables Productivity
- Unique Features
- Effective Service
Vaishnavi K.
- Role: Information Technology
- Industry: Agriculture
- Involvement: Vendor Management and Renewal
Submitted Feb 2024
Fantastic product
Likeliness to Recommend
What differentiates Google BigQuery from other similar products?
Fast processing and easy to use
What is your favorite aspect of this product?
Easy to use
What do you dislike most about this product?
Platform depends
What recommendations would you give to someone considering this product?
Must try and best exicution
Pros
- Helps Innovate
- Continually Improving Product
- Effective Service
- Inspires Innovation
Yash S.
- Role: Information Technology
- Industry: Finance
- Involvement: IT Leader or Manager
Submitted Dec 2023
Google BigQuery: Powerful Analytics Platform
Likeliness to Recommend
What differentiates Google BigQuery from other similar products?
Google BigQuery sets itself apart from other similar products through its exceptional speed and scalability in processing large-scale datasets. The platform's serverless architecture eliminates the need for infrastructure management, enabling users to focus solely on querying and analyzing data. Its unique billing model, based on on-demand pricing, ensures cost-effectiveness by charging only for the resources used during queries, making it a flexible and cost-efficient solution. Additionally, seamless integration with other Google Cloud services and robust support for real-time data
What is your favorite aspect of this product?
As a data engineer, what truly impresses me about Google BigQuery is its lightning-fast processing speed and hassle-free scalability. The way it effortlessly handles large datasets, allowing me to focus on data engineering tasks without worrying about infrastructure management, is a game-changer. The serverless architecture and pay-as-you-go pricing model make it not only efficient but also cost-effective. Moreover, the seamless integration with other Google Cloud services adds an extra layer of convenience to my workflow. Google BigQuery, with its speed, scalability, and ecosystem integration, has become my go-to tool for efficient pipeline
What do you dislike most about this product?
While Google BigQuery excels in many aspects, one area that can be challenging is the cost, especially for large and complex queries. The on-demand pricing model, while flexible, can lead to unpredictability in expenses, and users need to be mindful of optimizing queries to manage costs effectively. Additionally, there might be occasional limitations in terms of advanced data preparation features within the platform, requiring users to perform some tasks outside of BigQuery.
What recommendations would you give to someone considering this product?
For those considering Google BigQuery, I would recommend carefully assessing your data engineering needs and ensuring compatibility with the platform's strengths in speed and scalability. Take the time to understand the on-demand pricing model and optimize your queries to manage costs effectively. Leverage the seamless integration with other Google Cloud services to maximize the platform's potential within your ecosystem. Additionally, stay updated on new features and enhancements, as Google Cloud regularly introduces improvements to the platform.
Pros
- Continually Improving Product
- Performance Enhancing
- Enables Productivity
- Unique Features
