Data analytics experience on Google BigQuery
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Starting at $6.25 per TiB (after the 1st 1 TiB per month, which is free)
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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.
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Popular Features
- Database scalability (50)8.888%
- Database security provisions (43)8.787%
- Automated backups (24)8.585%
- Monitoring and metrics (45)8.484%
Reviewer Pros & Cons
Pricing
Standard edition
$0.04 / slot hour
Cloud
Enterprise edition
$0.06 / slot hour
Cloud
Enterprise Plus edition
$0.10 / slot hour
Cloud
Entry-level set up fee?
- No setup fee
For the latest information on pricing, visithttps://cloud.google.com/bigquery/prici…
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Starting price (does not include set up fee)
- $6.25 per TiB (after the 1st 1 TiB per month, which is free)
Product Demos
Lesson#6 - BigQuery for beginners| Analyzing data in google bigquery | Step by step tutorial (2020)
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How to get started with BigQuery
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BigQuery, IPython, Pandas and R for data science, starring Pearson
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Google BigQuery Demo
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Google BigQuery introduction by Jordan Tigani
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Product Details
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- Competitors
- Tech Details
- FAQs
What is Google BigQuery?
Google BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data. At the core of Google’s data cloud, BigQuery can be used to simplify data integration and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make an organization’s operations more data-driven.
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.
Store 10 GiB of data and run up to 1 TiB of queries for free per month.
Gemini in BigQuery for an AI-powered assistive experience
BigQuery provides a single, unified workspace that includes a SQL, a notebook and a NL-based canvas interface for data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. Gemini in BigQuery provides AI-powered assistive and collaboration features including code assist, visual data preparation, and intelligent recommendations that help enhance productivity and optimize costs.
Bring multiple engines to a single copy of data
Serverless Apache Spark is available directly in BigQuery. BigQuery Studio lets users write and execute Spark without exporting data or managing infrastructure. BigQuery metastore provides shared runtime metadata for SQL and open source engines for a unified set of security and governance controls across all engines and storage types. By bringing multiple engines, including SQL, Spark and Python, to a single copy of data and metadata, the solution breaks down data silos.
Built-in machine learning
BigQuery ML provides built-in capabilities to create and run ML models for BigQuery data. It offers a broad range of models for predictions, and access to the latest Gemini models to derive insights from all data types and unlock generative AI tasks such as text summarization, text generation, multimodal embeddings, and vector search. It increases the model development speed by directly applying ML to data and eliminating the need to move data from BigQuery.
Built-in data governance
Data governance is built into BigQuery, including full integration of Dataplex capabilities such as a unified metadata catalog, data quality, lineage, and profiling. Customers can use rich AI-driven metadata search and discovery capabilities for assets including dataset schemas, notebooks and reports, public and commercial dataset listings, and more. BigQuery users can also use governance rules to manage policies on BigQuery object tables.
Google BigQuery Features
Database-as-a-Service Features
- Supported: Database scalability
- Supported: Database security provisions
- Supported: Monitoring and metrics
Google BigQuery Screenshots
Google BigQuery Video
Demo: Solving business challenges with an end-to-end analysis in BigQuery
Google BigQuery Competitors
Google BigQuery Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
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.
Google BigQuery starts at $6.25.
Snowflake, Amazon Redshift, and Databricks Lakehouse Platform are common alternatives for Google BigQuery.
Reviewers rate Database scalability highest, with a score of 8.8.
The most common users of Google BigQuery are from Enterprises (1,001+ employees).
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Reviews and Ratings
(246)Attribute Ratings
Reviews
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March 25, 2024
Data analytics experience on Google BigQuery
- Provide real time data for analysis and monitoring purpose.
- SQL based queries makes it user friendly.
- It can handle large amount of data.
- sometime faced performance issues in query execution
- training material is not easily available
- Continuous maintenance required
March 21, 2024
Great tool for Data Warehousing & Storage!
- Good place to store historical data.
- It has free connectors to other Google platforms like Looker, which makes it easy to use as a data source.
- User interface is easy to navigate.
- Hard to find data if you don't know where everything is hosted.
- If you have to upload excel files it takes so long.
- If you aren't a technical users you likely won't know how to use BigQuery effectively.
March 17, 2024
Google BigQuery Review
- Reading and analyzing data
- Easy access management through GCP
- Export data easily to further tools such lookers and spreadsheets
- Query size warning
- Limitations to daily usage
- Best practices recommendations
March 17, 2024
BigQuery helps us remain a data driven team
- Mining large data sets
- Determining trends
- Strategize product depending on the trends
- It can be slow at times
- Could be difficult for a first time user
March 15, 2024
A good big-data powerhouse for analytics and more
- Store large amounts of semi-tabular data
- Allows complex and fast queries
- Allows streaming of data from different sources
- Unstructured data is complex to query
- Costs can be high if using large data sets
- It's hard to estimate costs as they depend on usage
March 15, 2024
My Experience with Google BigQuery.
- Scale automatically to handle datasets of any size.
- BigQuery can perform extremely fast SQL queries across vast datasets.
- Pay-as-you-go model, BigQuery allows users to pay only for the data processed and stored.
- It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
- The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
March 14, 2024
Big Query, very powerful in the right context.
- Good python package.
- SQL knowledge goes a long way though some peculiars are confusing.
- Make it more simple to administrate login from python.
- Difficult to estimate cost prior.
March 13, 2024
Google BigQuery - Experience
- Ease of use
- Scalability
- Lots of integrated Google Cloud features
- Query Latency
- Indexing
- Few errors when exporting data to buckets
March 13, 2024
Data Warehouse That Gives Your Organization wings!
- Data warehouse
- Complex queries
- Server-less
- Real-time queries
- The speed at which the queries run
- Suggesting insights from within the data automatically
- Making it simpler for a non-tech person to access it
March 13, 2024
Average product and does what it says it can
- Fast Query Engine
- Useful Documentation
- similar syntax to SQL server
- UI - its not the nicest UI
- Original setup can be challenging
- Depending on how you use it can become expensive
March 12, 2024
Google BigQuery review
- Syncing with Google products, e.g. Looker studio. Easy to create dashboards when putting a Google BigQuery data table as data source.
- Scalability. It allows many opportunities across the business.
- It's easy enough to write SQL statements front-end to explore the data tables.
- Interface difficult to understand for new users.
- Not much support provided.
- Having to wait roughly 24 hours before getting the data from Google Analytics into Google BigQuery. A shorter time would be great.
March 12, 2024
A data powerhouse to manage complex datasets in a jiffy
- Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
- Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
- Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
- Google BigQuery's built-in visualization tools are limited compared to dedicated BI tools. Expanding the options and allowing for more customization would help explore and present data insights.
- Currently, it's hard to track where the data comes from and how it changes as it moves through the pipeline because it lacks data lineage capabilities. It's tough to ensure data quality assurance and regulatory compliance.
- The current access control options are somewhat limited. Granular control over specific datasets or tables within a project would help manage access in collaborative environments.
- Data Warehousing
- Data Analytics
- Machine Learning
- 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.
March 12, 2024
Queries made easy with Google BigQuery
- Managing Data
- Complex Queries (SQL dialect supported)
- Integration capabilities with Google products
- User interface might be complex for newbies
- Access management confusing and tight with IAM roles
- Can be expensive for different workloads types
March 12, 2024
Google BigQuery can be awesome!
- Performance
- Setup
- Security
- Ease of Use
- User interface
- Dependencies to other solutions
March 12, 2024
Store & Analyze your data with Google BigQuery
- Store Large data set
- Very minimal Charge for Storage
- You can write SQL queries on Google BigQuery
- There is no training module for Google BigQuery for that reason newcomers will face problems with the user interface and not be familiar with the syntax of SQL query format of Google BigQuery
- There are some functions which are only used in Google BigQuery which I feel difficult to understand and no one is there for you on how it will work so I think there should be some customer support team would be there where you can raise your concerns with the team.
- Side-by-side view of tabs for easy comparisons
- Ability to open multiple tabs to switch through different pieces of code
- Easy to understand layout of projects and tables
- More detailed descriptions of errors when running code
- Ability to export larger files as csv
March 12, 2024
Great Data Warehouse.
- Storage
- Error Checking.
- Organization
- Global Query Search.
- Query Scheduling.
- UI Speed.
March 12, 2024
Great platform
- Efficiently analyzes large datasets
- Shallow Learning Curve
- Offers more flexibility/customization
March 12, 2024
Data Analysis on Steroids with Google Big Query
- Big Query is fast and based on the cloud you can run your query on a huge dataset. Huge means data in TB's. This also reduces the company cost to build that kind of infrastructure to store data.
- Not specific to Google Analytics but you can import data from different sources for analysis purpose and use the power of the cloud to run the query.
- Not much time to learn - You don't need any special skills, just SQL and you can use Big Query for your use. Learning SQL is not a big task you can learn it in a week.
- Big Query refrence schema and different sample query are available to practice on queries.
- Google also provide sample dataset to use then purchase Big Query.
- Though it is SQL some syntax are different but they are getting used to after you use for some time.
- The legacy SQL is in beta state but can be used and you can run the query with simple SQL.
- More documentation is needed for using User-defined functions in Big Query.
- Allows for fast and efficient analysis of huge amounts of data
- Allows for running interactive and batch queries
- Allows for creation of dashboards and reports
- Allows for real-time analytics on a server-less architecture
- Streaming data can be expensive
- Does not support advanced Machine Learning and Deep Learning techniques
- Number of partitions in tables are limited to 4,000
December 21, 2022
One stop solution for data management and fast querying
- Automatically optimises queries to fetch data quickly
- Allows efficient management of data across multiple databases
- The editor and query builder have a very intuitive interface that makes it easy to build new queries fast
- Not able to search specific column fields using search functionality
- Uploading database using excel is time consuming and error prone
- The error message thrown while querying can be more customisable to correct the errors
December 20, 2022
Google BigQuery is a great tool for basic data warehousing.
- Inexpensive data storage.
- Relatively easy to use interface once you get used to it.
- Inexpensive query costs.
- Good number of native integrations.
- Difficult to use interface if you're not used to it.
- User management has proven confusing when trying to add new people to projects/accounts.
- There is no user support, which is a huge issue.
December 19, 2022
Google BigQuery is ok!
- Cloud based architecture rather than client based architecture
- There is a free trial
- Google product so the support is very good
- Most organizations use SQL so it is a bit of an adjustment
- No other major issues - serverless data is great and hard to frown upon
- Large queries run well in the program
May 05, 2022
Best Serverless SQL Database
- Data Warehousing
- Sporadic SQL queries without having to manage instances
- Reporting directly consumed from views
- A better output data exploration tool
- Better handling of parquet files
- An own application with a more comfortable UI than in the browser