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Google BigQuery

Google BigQuery

Overview

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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Recent Reviews

TrustRadius Insights

Quick Data Analysis: Users appreciate the rapid query speed of Google BigQuery, enabling them to analyze massive datasets without long …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

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  • Database security provisions (47)
    8.9
    89%
  • Database scalability (54)
    8.8
    88%
  • Automated backups (24)
    8.5
    85%
  • Monitoring and metrics (49)
    8.4
    84%

Reviewer Pros & Cons

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Pricing

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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)
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Product Demos

Lesson#6 - BigQuery for beginners| Analyzing data in google bigquery | Step by step tutorial (2020)

YouTube

How to get started with BigQuery

YouTube

BigQuery, IPython, Pandas and R for data science, starring Pearson

YouTube

Google BigQuery Demo

YouTube

Google BigQuery introduction by Jordan Tigani

YouTube
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Features

Database-as-a-Service

Database as a Service (DBaaS) software, sometimes referred to as cloud database software, is the delivery of database services ocer the Internet as a service

8.4
Avg 8.7
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Product Details

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

Screenshot of 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.Screenshot of 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.Screenshot of 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.Screenshot of 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.Screenshot of 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.Screenshot of 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.Screenshot of 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.Screenshot of 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.

Google BigQuery Video

Demo: Solving business challenges with an end-to-end analysis in BigQuery

Google BigQuery Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

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 Data Intelligence Platform are common alternatives for Google BigQuery.

Reviewers rate Database security provisions highest, with a score of 8.9.

The most common users of Google BigQuery are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews and Ratings

(252)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Quick Data Analysis: Users appreciate the rapid query speed of Google BigQuery, enabling them to analyze massive datasets without long wait times. The fast query performance is a significant advantage highlighted by users for efficient data processing and analysis.

User-Friendly Interface: Many reviewers find Google BigQuery very user-friendly, allowing team members with varying levels of expertise to easily query data using simple language. The intuitive interface of Google BigQuery's editor and query builder is noted as helpful in quickly constructing new queries by users.

Seamless Integration: Users value the seamless integration of Google BigQuery with other tools like Google Cloud Storage and Data Studio, enhancing workflow efficiency and collaboration. This integration capability with various tools contributes to improved data management solutions according to users' feedback.

Challenge in Prompt Data Deletion: Users have encountered difficulties in promptly removing new data streams, which can lead to inefficiencies and waiting times during the deletion process. This issue may impact users' ability to manage their data effectively and maintain a streamlined workflow. Enhanced UI Visibility Needed: Several reviewers have suggested enhancing data visibility within a single page through UI improvements for better user experience. They seek clearer presentation of data on one screen without the need for excessive navigation, enabling quicker access to essential information. Simplified Security Integration Requested: Customers have called for easier management of security credentials and seamless Tableau integration without frequent re-authentication hassles. Simplifying security processes would enhance user convenience and potentially improve overall system usability.

Attribute Ratings

Reviews

(1-25 of 54)
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Score 9 out of 10
Vetted Review
Verified User
Incentivized
My company relies on Google BigQuery to manage and analyze our vast datasets effectively. As a DevOps engineer, I recommend my colleagues to choose Google BigQuery over other alternatives. Google BigQuery serves as our central data warehouse. It ingests, stores, and optimizes large volumes of data, allowing us to perform complex queries efficiently. Whether it’s historical sales data, customer behavior, or inventory records, Google BigQuery handles it seamlessly.
  • Scalability and Speed: Google BigQuery handles large-scale data processing with ease
  • Serverless Architecture , so no infra management
  • Geospatial Analysis
  • Integration with Ecosystem as my company uses Google cloud platform
  • Cost-Effective Pricing
  • Queries that haven’t been optimised for speed or return redundant data can become expensive. So, cost estimation feature would be great!
  • Google BigQuery lacks robust built-in data visualisation tools. Integration with GCP is seamless, but third party integration would be beneficial for visual dashboards.
  • In my opinion, Google BigQuery schema changes can sometimes be cumbersome, especially for large tables. Simplifying the process of adding, removing, or modifying columns could improve data management workflows.
1.Google BigQuery stores and analyses massive datasets in my organisation, making it ideal for me , as i can manage Terrabytes of data with it.2.Google BigQuery can ingest and analyze streaming data feeds in near real-time, so it helps to make data-driven decisions very fast. As for less appropriate scenarios:- 1. For very small datasets (in the megabyte range), traditional relational databases or spreadsheets might be more cost-effective and easier to manage. 2. Also, in scenarios where frequent schema changes is needed, it becomes cumbersome.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
The scope of my use case is data governance relevant. My organization uses Google big query as the primary tool for storing, reading, updating and analyzing data. Due to the big size of the organization, we have an increasing number of data consumers and my specific use case is to provide a data control panel to make sure the data is being used properly according to the data Governance policies.
  • 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
Google big query is perfect for simple and fast use-cases where users need to access data quickly and and seamless. GCP IAM makes it easy to have a control on who can access the data and and provides services accounts to automate jobs. Which then makes it easy to have an overview on th data consumption.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We are using Big Query to store metric data of our chatbot. It helps to get all the data in a single place and its easy to manage. The data is generated by the chatbot every time so we needed a scalable, cost-effectiveness and fast data processing database, thats why we use big query for it. Its integration with other software are also easy to do with lots of documentation.
  • storage of structured data
  • query execution speed
  • volume of data stored and processed
  • availability and latency
  • cannot delete new data due to streaming, i have to wait some time to delete new data
  • the UI can be improved
  • not able to see all data in a single page
Use it if you have to process large data and complex query in short time. The pay-as-you-go pricing model ensures cost-effectivenes. If you need low latency use it.
Liz Brandon | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
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.
  • Data management
  • Data connection
  • Data warehousing
  • Data access
  • Data certification
  • Provide easier management of security credentials
  • More seamlessly integrate with Tableau without the constant need to re-authenticate
Google BigQuery has worked perfectly fine for our needs. It is easy to manager data and make reporting tables available to users throughout the company. We are able to create certified data sources and customize them to include exactly the needed information. Data refreshes fairly quickly do that we can keep all of our reporting up to date. Google BigQuery has enabled greater self service analytics capabilities within my company as now anyone can connect to the same certified data source.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
We use Google BigQuery in conjunction with Bloomreach, this allows us to query the back end of the data without having to use the front end. The tool is fast to run queries and allows us to move the data to our other Data Warehouse environments quickly with little effort.
  • 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
Google BigQuery handles big data sets really well and has a solid enginge to query and maniulapte the data. The syntax is easy to pick up if your use to other database languages like SQL server but there are some syntax differences. Once setup it is a simple product to use and utilise
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Our company uses Google BigQuery to sort and track accounting information which is related to business transactions. We use the integrations available through Google BigQuery to directly import this data and sort it for use in our own custom-made tools to manage financing data in our company. Google BigQuery's seamless integration with the Google Workspace platform allows us to access this data across multiple platforms and filter and sort data in meaningful ways.
  • Data Query
  • Active Database Management
  • Integration with other Programs
  • Navigation of side panel can be tedious at times
  • Ability to deploy queries more easily across multiple datasets
  • More step-by-step guides (the ones they have are great)
Google BigQuery is a fantastic tool for exporting and importing data from different programs. As organizations grow and utilize multiple different platforms, the ability to move large datasets between those platforms is incredibly valuable. Users capable of performing database queries can quickly access this data and use it in meaningful ways. However, users that don't understand the limitations of databases within programs that Google BigQuery can export information to will find themselves struggling to utilize it effectively. Many of our users heavily employ spreadsheets for business tracking, but as datasets become larger spreadsheets become cumbersome, and attempting to use spreadsheet formulas on database information does not translate well.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
In our company, we use Google BigQuery to make analyzing data easier and help us make better decisions. It's great because it lets us look at big amounts of data quickly without needing lots of complicated setups. Anyone in our team can use it because it's simple to understand and helps us find important information from our data. We also connect it with other tools we use to make our work smoother. We use it for things like understanding how our team is doing, seeing how people use our products, some teams use it for managing their finances, and keeping track of how well systems are running. All in all, Google BigQuery is really important for us to do our work well.
  • First and foremost - Google BigQuery is great at quickly analyzing large amounts of data, which helps us understand things like customer behavior or product performance without waiting for a long time.
  • It is very easy to use. Anyone in our team can easily ask questions about our data using simple language, like asking ChatGPT a question. This means everyone can find important information from our data without needing to be a data expert.
  • It plays nicely with other tools we use, so we can seamlessly connect it with things like Google Cloud Storage for storing data or Data Studio for creating visual reports. This makes our work smoother and helps us collaborate better across different tasks.
  • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
  • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
  • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over.
However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.
Rajender Singh | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We are analyzing large volumes of data generated by IoT devices to derive actionable insights and improve decision-making and for monitoring purpose while sitting from different places around the globe. Google BigQuery is helping us in setting up automation of gear manufacturing process in factories so as to reduce human effort.

  • 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
Google BigQuery we have used while processing large amount of data when connected with Iot devices in automation factory which continuously give real time data and Google BigQuery can handle it very easily.

Sometime Small volume of data require same effort of writing query which is little bit hectic for users.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
My company, Randstad, uses BigQuery as our data warehouse to store all our lead information and marketing metrics. It pulls numbers from various sources and then creates master data sources, which we use for the performance dashboards we present to internal stakeholders. More recently, we have been using Big Query to host our historical data from Google Analytics.
  • 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.
BigQuery has been a great product for getting information from many different sources. We can use BigQuery to connect/join other sources together and find ways to match the data together to have a master data source. There have been times when we have used it, though, when I do not think it was needed and it was probably more of a headache.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use BigQuery majorly for two purposes. Our data engineering team develops trends based on collected data over BigQuery. That helps us strategize our feature rollouts. The second use case where we make use of BigQuery is in our tests dashboard. We collect test success and failure data and use BigQuery to categorise different failures, calculate failure rates and show trend for errors after weekly releases.
  • 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
Google BigQuery is suitable for use cases where there is a need for continuous data collection and one would want to mine that data, derive trends and behavioral data based on set parameters.
Nir Levy | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We are using Google BigQuery to store and analyze our big-data and analytics for one of our major projects. We stream different types of data from different sources into BQ and use complex queries to join data from different sources. Data can be queried both programmatically from our application, or displayed using tools like Looker Data Studio.
  • 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
I would use Google BigQuery for storing data warehouse information, streaming from multiple sources, and displaying either in my application's dashboard, Looker Studio, or Grafana. It's very easy to stream data from Firebase to BQ, and very effective as well. It is hard to stream data from your main database, and requires some work, but I believe it is worth the effort.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We have used Google's big query to store and analyze vast amounts of data. In today's time, every organization requires real-time insights from the data. BigQuery can be Integrated with popular BI tools to visualize data and generate actionable insights, aiding in department decision-making processes. With BigQuery, we have a centralized repository for all organizational data, facilitating easy access for analysis and reporting.
  • 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.
For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
As a supplement solution to the main enterprise systems for reporting, it is mostly used for the R&D department. The aim was to query rather diverse and semi-structured data from various systems. Some of the sources were wide, some deep and a few were both. Other tools for storing and querying were tried as well.
  • 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.
Good for large datasets where query performance is otherwise an issue. It is bad for diverse data sources that are not large enough to really benefit and are overkill. Similar to use cases where many users need to query infrequently, where the minor syntax differences between SQL and Google Big Query can be annoying.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Due to its lower cost, we use BigQuery as our primary database to store most of our data. We also use BigQuery to run periodic analytical tasks. We mainly use it for our WebSights product which collects and stores many user demographics and enriches IP traffic.
  • Ease of use
  • Scalability
  • Lots of integrated Google Cloud features
  • Query Latency
  • Indexing
  • Few errors when exporting data to buckets
Google BigQuery integrated really when with a product that generates enormous amount of data, since appending data to BigQuery is much faster even in high frequency. They also offer a generous free tier which helps in determining its suitability and costs scale according the usage. If you need a really low latency query execution, this might not be what you are looking for.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Google BigQuery is being used in unlocking real-time user data and boost data-processing power to perform more extensive business analytics. Along with other complimenting products like Dataplex, it has become a solid warehouse for the whole organization to make data-backed decisions.
  • 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
Good for:
- Unlocking real-time user data
- Boosting data-processing power
- Performing more extensive business analytics

Not so good for:
- Transactional data
- Updating data
Score 7 out of 10
Vetted Review
Verified User
Incentivized
We use Google BigQuery as a data warehouse to pull data from analytics platforms such as Google Analytics. This allows us to create various tables containing the exact data various parts of the business need. We can then create dashboards for end-users internally. It especially answers our needs in terms of user behaviour and engagement. Our data capabilities are reinforced and much more scalable.
  • 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.
Google BigQuery is suited to easily sync/connect different Google products for analytics purposes. Google BigQuery is a great data warehouse if a business use Google Analytics. It also allows more autonomy to various end users with diverse technical knowledge to create dashboards independently in Google Data Studio (now Looker Studio).
Deep Mukherji | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We deal with massive datasets – customer transactions, website logs, sensor data from our products – all running into terabytes. Google BigQuery acts as our central data warehouse and ingests data from various sources, like CRM systems, marketing tools and also from internal applications. It's not just the marketing team or data scientists who leverage it. Sales uses it for customer segmentation and churn analysis. The product team relies on it for user behaviour analysis and identifying feature adoption trends. The speed of Google BigQuery is mind-blowing. I can run complex SQL queries on massive datasets and get results almost instantly.
  • 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.
If you've already invested in the Google Cloud ecosystem and since Google BigQuery is part of the Google Cloud Platform (GCP), it easily integrates with other GCP services like Cloud Storage for data storage and Cloud Data Studio for data visualization. We only pay for the resources we use, unlike traditional data warehouses with fixed costs regardless of usage, thanks to its pay-per-use pricing model with no upfront investment and ongoing maintenance.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
  • 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.
Google BigQuery is well suited if you have TB or PBs of data which needs to be analyzed with accuracy and then you need to find trends or create dashboards as it has seemless integration with Looker.

Google BigQuery is not well suited if your Database is very small. As the Google BigQuery architecture take similar time in small database which is counter intuitive.
Ilyas Bakirov | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We tried to use Google BigQuery to analyze, perform and build various custom queries to our large set of geological historical data. To solve our needs in geological analysis of huge data, we looked around at what tools would allow us to optimally perform analytical work without capital expensenses and learning new tool.
  • 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
Managed service without any capital investment for users. New users must have knowledge of BigQuery and SQL in order to use it correctly and for its intended purpose. Also scales well and groups according to the size of the dataset and tasks.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We are using Google BigQuery to store all events data of our app and Since our events data are recorded every second wise so it's a large data set of events that are easily handled by Google BigQuery since Google BigQuery has minimal charges for storage and mainly it will charge for running the query inside the Google BigQuery so it will be very easy for us to store a large database in 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.
As previously mentioned Google BigQuery is perfect for storage of you have large data sets since they are charging very minimal charge for storage but they will charge for every single query that you run on Google BigQuery so if you have large data sets then go for it. If you want to do query on the data then Google BigQuery will already provide and you can also build the dashboard with your data on Google Data Studio.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Our business uses Google BigQuery to analyze data from Google Analytics (both UA and GA4). The platform has an easy to understand layout that has improved a lot over the years. One of the key features that makes it user friendly is the ability to have side-by-side tabs of different code and output. This makes it easy to compare multiple versions of data. This platform is used to help us track are key web vitals that inform on us our sites performance. Because we have multiple different key variables that are stored in different locations, we often need to join data tables together or compare data between the different locations.
  • 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
Google BigQuery is great when you have a large body of information that needs to be analyzed. It provides an estimate of how much data is going to be queried which can help you identify if you need to optimize your code further before running.
March 12, 2024

Great Data Warehouse.

Score 8 out of 10
Vetted Review
Verified User
Incentivized
We use BigQuery as our data warehouse to store a large part of our data. We also use BigQuery to normalize, tie together, and prepare data for data visualization. It allows us to tether disparate data sources to create analyzable and comprehensive KPIs at granular and high-level layers.
  • Storage
  • Error Checking.
  • Organization
  • Global Query Search.
  • Query Scheduling.
  • UI Speed.
BigQuery is great for organizing and preparing data for data analysis, reporting, and visualization. Using Standard SQL to query data within the data warehouse is a comprehensive and resource-rich language that is easy to use and robust. It is very helpful when multiple data sources must be strung together for analysis.
March 12, 2024

Great platform

Score 9 out of 10
Vetted Review
Verified User
Incentivized
Google BigQuery serves as our essential PaaS tool for streamlined data management and analysis. As a serverless solution, it offers automatic scaling, eliminating infrastructure hassles. Leveraging its advanced capabilities, we efficiently process large datasets through SQL queries. This empowers our organization with rapid, insightful decision-making, fostering a dynamic, data-driven approach that enhances overall operational efficiency and strategic planning.
  • Efficiently analyzes large datasets
  • Shallow Learning Curve
  • Offers more flexibility/customization
I would rate 9 out of 10. The platform's user-friendly interface and ease of learning make it accessible for various team members. Its exceptional capability to handle big data seamlessly aligns with our diverse analytics needs. The serverless architecture streamlines operations, enhancing overall efficiency. While there's room for slight improvements, Google BigQuery remains a valuable asset, significantly impacting our data analytics and decision-making processes.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We are the reseller of Google Analytics and with Google Analytics premium you get Big Query. You get 500$ credit to use in Big Query. Big Query is a great tool to get unsampled reports, that can be further used for different analysis also to build products on top of it. Big Query can help you to analyze user journey, enhanced eCommerce data for creating remarketing audience. You just need to know SQL and you can use Big Query to get whatever data you want. Big Query can be further utilized for your own purpose, you can upload your CRM data and map with Google Analytics data.
  • 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.
- If you are using Google Analytics and there is huge data that is getting streamed every day then you must have Big Query and use it for analysis. It is not only helpful for analysis but also for debugging your Google Analytics implementations.
- For analyzing a small dataset you don't need Big Query you can use normal MySQL on your own premises. Analyzing on Un-structured data is not possible with Big Query.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
My organization is primarily concerned with training individuals to use store and analyze large amounts of data in a manner that is fast and accurate. Google BigQuery makes it possible to use the Cloud's infrastructure (hardware and software) to accomplish its data analysis goals. Being able to pay for the time and space that is utilized offers significant cost savings, especially for smaller (and mid-size) businesses and those that do not possess adequate resources for establishing a high-capacity infrastructure.
  • 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
I found Google BigQuery very easy to use from the very beginning. Users do need a very good knowledge of SQL in order to write queries that are processed efficiently. Using Select * queries can bog down resources and drive up costs.
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