What is Google BigQuery?
BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you have no infrastructure to manage and don't need a database administrator, use familiar SQL and can take advantage of pay-as-you-go model.
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Real user data aggregated to summarize the product performance and customer experience.
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87 Likeliness to Recommend
96 Plan to Renew
90 Satisfaction of Cost Relative to Value
Emotional Footprint Overview
+90 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
- Caring
- Acts with Integrity
- Enables Productivity
Emotional Footprint
How to Read
Positive
Neutral
Negative
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
Feature Ratings
Data Management
Data Integration
Real Time Capabilities
Distributed Processing
Metadata Management
Analytics and Data Science Tools
Platform Administration
Data Security
Workload Management and Monitoring
Analytics and Reporting
Data Visualization
Vendor Capability Ratings
Vendor Support
Business Value Created
Ease of Implementation
Quality of Features
Breadth of Features
Ease of Data Integration
Ease of IT Administration
Usability and Intuitiveness
Availability and Quality of Training
Product Strategy and Rate of Improvement
Ease of Customization
Google BigQuery Reviews
Elena S.
- Role: Information Technology
- Industry: Technology
- Involvement: End User of Application
Submitted Jan 2023
Ideal substitute for conventional data warehouses.
Likeliness to Recommend
What differentiates Google BigQuery from other similar products?
The best option, which can save a ton of time, is offered by Google Cloud BigQuery, which also helps with building ML models using only SQL queries. The capability to import data from CSV and other sources is one of its outstanding features. We enjoy working with it.
What is your favorite aspect of this product?
Google Cloud BigQuery is among the most effective options for the data warehouse process. Because it is so easy to use, even a beginner can utilize the device in one sitting. The ability to build ML models using a query is one of its better features and one that I really liked. It makes it very simple to manage large and complex searches.
What do you dislike most about this product?
Bigquery bases its estimation of the cost of a query on the volume of data that has to be processed, hence, it is easy to make costly searches without recognizing it. The number of rows returned by the query will not change, even if I limit it. The new Bigquery SQL editor's autocomplete feature occasionally produces unwanted outcomes.
What recommendations would you give to someone considering this product?
It was first and foremost a fantastic tool that met all of our demands. For our employees, it makes it very easy to manage extensive and complicated searches, and one of its best features, and a feature I particularly like is the capability to build machine learning models using a query. I wish to suggest this to you on their behalf.
Pros
- Helps Innovate
- Performance Enhancing
- Enables Productivity
- Trustworthy
Austin W.
- Role: Human Resources
- Industry: Technology
- Involvement: End User of Application
Submitted Jan 2023
Less sophisticated yet effective data analysis
Likeliness to Recommend
What differentiates Google BigQuery from other similar products?
Google BigQuery is well-liked on the front end because it is cost-effective and easy to use, but when it comes to execution, it is an enterprise data warehouse that is totally serverless, has integrated ML and BI is cross-cloud compatible and expands with all of our business data. We can access and use our data wherever it is by using BigQuery, which eliminates the need for a separate repository to store data.
What is your favorite aspect of this product?
I was able to easily analyze data from the many clouds in a secure, quick, and convenient manner using the Google BigQuery platform, and I was able to disseminate only the outputs that were required and of high quality using a single user interface. With the help of this wonderful technology, we can now dramatically streamline our research, learn new things about spatial data, and build entirely new business potential.
What do you dislike most about this product?
The UI did not particularly appeal to me; in my opinion, it should be more understandable, simple to use, customizable, and logical. Except for it, the query quality is excellent and all other operations go without a hitch. I want to design our dashboards as rapidly as possible based on the needs that are currently a little overwhelming.
What recommendations would you give to someone considering this product?
BigQuery is a tool that can be helpful for massive data as its name implies. Since everything can be done inside the BigQuery Platform with a single touch, using manual ML models is no longer necessary to transform large amounts of raw data into profitable analytics. In contrast to competitors, it provides a less complex yet effective data analysis store that will help business expansion and earnings.
Pros
- Helps Innovate
- Enables Productivity
- Effective Service
- Inspires Innovation
Christ W.
- Role: Finance
- Industry: Finance
- Involvement: End User of Application
Submitted Jan 2023
An incredibly potent tool for storing statistics
Likeliness to Recommend
What differentiates Google BigQuery from other similar products?
Although data models design and sharing are both feasible through the user interface, I would also like a new feature that would allow me to directly compare analytical data to other outputs. Outside of Google Cloud, add-ons also require improvement. Even though we had an analytical data store, we've been fighting a data disaster at work for years because it was hard to implement and we weren't sure how to use it correctly. Google BigQuery has eliminated the hassle of data maintenance while also transforming our messy data into valuable business insights. It has established itself as our big data's analytical data store.
What is your favorite aspect of this product?
I am a data expert, and I found it useful in that it allowed me to quickly develop and execute machine learning models on our source data at a wider scale inside the Google BigQuery infrastructure, cutting my working time in half. It is a win-win situation because it is faultless, sophisticated, and fairly priced.
What do you dislike most about this product?
Even while the present techniques are effective, some of my clients would rather receive simple warnings when their payment goes over a specific threshold. It occasionally frustrates me that there are so few heuristic options, like grouping and reformatting, because I can't always find what I'm looking for.
What recommendations would you give to someone considering this product?
Any data expert working on a platform other than Google BigQuery is, in my opinion, missing out on the true quality and value of their data. I advise them to compare the effectiveness and results of both analytical data platforms; they will be astounded by Google BigQuery's features and functionality. Its reasonable pricing makes it accessible to even tiny businesses.
Pros
- Performance Enhancing
- Enables Productivity
- Effective Service
- Inspires Innovation