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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Google BigQuery

    Score8.8 out of 10
    N/AGoogle's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

    $6.25

    per TiB (after the 1st 1 TiB per month, which is free)

    Pricing
    Google BigQuery
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Offerings
    Pricing Offerings
    Google BigQuery
    Free Trial
    Yes
    Free/Freemium Version
    Yes
    Premium Consulting/Integration Services
    No
    Entry-level Setup FeeNo setup fee
    Additional Details—
    More Pricing Information
    Community Pulse
    Google BigQuery
    Considered Both Products
    Google
    Chose Google BigQuery
    Fully serverless. We don’t manage clusters or warehouses. Requires us to manage virtual warehouses. BigQuery is cheaper for exploratory heavy queries; Snowflake is more predictable for sustained workloads. BigQuery is unbeatable if you’re deep in Google’s ecosystem; Snowflake …
    Incentivized
    Chose Google BigQuery
    Google BigQuery is widely used for structured data.
    Incentivized
    Chose Google BigQuery
    Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with …
    Incentivized
    Chose Google BigQuery
    Compared to PostgreSQL and MySQL, Google BigQuery is faster and more scalable for large datasets. It’s serverless, so there’s no need to manage infrastructure. We chose Google BigQuery for its ease of use built-in analytics features
    Incentivized
    Chose Google BigQuery
    The architecture of ETL was influenced by Data processing component which is Dataproc and there was a need for easy Query console with Access control capabilities with lesser overhead in managing the permission. This made the decision to move with Google BigQuery compare to …
    Incentivized
    Chose Google BigQuery
    is much better as it’s easily accessible provides velvet documentation and fulfils all our needs as well as easily integrated into clients, environment
    Incentivized
    Chose Google BigQuery
    Google BigQuery is simpler and I say it has simpler UI too.
    If you have a clear long term ask , mainly business intelligence needs then Google BigQuery offers you good.
    If you need too much of features under a single cloud and you are ok to be lil clumsy then you can check …
    Incentivized
    Chose Google BigQuery
    I have used most of the data analytics platforms. Based on my work, I have found that the user interface of Google BigQuery is simple to navigate. I like the front view - ease of joining tables, and integration with other platforms.
    Incentivized
    Chose Google BigQuery
    Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale.
    The price was also much lower for our use case (internal data analysis).
    Incentivized
    Chose Google BigQuery
    For our usage, Google BigQuery is cheaper and more performant. The others have their place, but in certain scenarios, Google BigQuery is a better solution.
    Incentivized
    Chose Google BigQuery
    We actually use Snowflake and BigQuery in tandem because they both currently meet various needs. Redshift, however, has barely been used since our migration away from it. In the case of both Snowflake and BigQuery, they beat Redshift by a long shot. The main reasons are their …
    Incentivized
    Chose Google BigQuery
    PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
    Incentivized
    Chose Google BigQuery
    I came to use BigQuery from a traditional system like MS SQL server, the features which are available in BigQuery as a cloud service far outweigh the features from SQL server. I have not used other similar tools like Amazon Redshift but Google BigQuery serves multiple use cases …
    Incentivized
    Chose Google BigQuery
    Google BigQuery is cheaper and much faster as compared to both. While as compared to Snowflake , we tested it was faster and cheaper by 30%, that is after Snowflake tweaked their environment, if not for that it would have been 90% cheaper than Snowflake. Redshift is not easy …
    Incentivized
    Chose Google BigQuery
    In my opinion, Google BigQuery is custom made to be the best data lake system that is easy to use, scalas to fit any business size, has inbuilt security, as well as tools for data integrity. Although a few other tools have some of the same functionality, Google BigQuery is the …
    Incentivized
    Chose Google BigQuery
    It's easier to connect data between BigQuery and Looker Studio instead of connecting the data between BigQuery and Tableau in terms of data explore or dashboard creating. Therefore we are considering migrating dashboards from Tableau to Looker Studio for the whole company.
    On …
    Incentivized
    Chose Google BigQuery
    When comparing Google BigQuery and Databricks, both platforms are powerful tools for managing and analyzing large datasets. BQ is ideal for businesses requiring large-scale analytics, reporting, and dashboarding with minimal operational overhead. It’s also great for ad-hoc …
    Incentivized
    Chose Google BigQuery
    Google BigQuery's main advantage over its direct competitors (Amazon Redshift and Azure Synapse) is that it is widely supported by non-Google software, while the others rely heavily on their own cloud ecosystems.
    Incentivized
    Chose Google BigQuery
    I have used other data manipulation tools like SQL Server and Google BigQuery feels more intuitive, Google provides so much documentation and tutorials that getting to know the software is not only easy but even satisfactory, so I'd say Google BigQuery is very superior to that …
    Incentivized
    Chose Google BigQuery
    Main reason is how it integrates directly with the google ecosystem which really facilitates the automatization proceses for the whole company. This ensures that sales and all the other departments have the correct information on a daily bases with a ease of use with day to day …
    Incentivized
    Chose Google BigQuery
    Amazon Redshift was a likely alternative we were considering , but it needs to be provisioned on cluster and nodes, which increases infrastructure management, whereas Google BigQuery is serverless, so no infra management :) Also, I remember when comparing them we did found out …
    Incentivized
    Chose Google BigQuery
    Its same as compared to Big query. We go with big query because of clients requirements in project.
    Incentivized
    Chose Google BigQuery
    Google BigQuery as a platform allows for more integrations and customizability than many other offerings. Users mostly need to understand the basics of database and SQL programming in order to get the most from the product. However, other products like Hevo do have less of a …
    Incentivized
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    Features
    Google BigQuery
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Automatic software patching8.017 Ratings
    Database scalability9.079 Ratings
    Automated backups8.524 Ratings
    Database security provisions8.873 Ratings
    Monitoring and metrics8.675 Ratings
    Automatic host deployment8.013 Ratings
    Best Alternatives
    Google BigQuery
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    All AlternativesView all alternatives
    User Ratings
    Google BigQuery
    Likelihood to Recommend
    9.0
    (79 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    Usability
    6.6
    (6 ratings)
    Availability
    7.3
    (1 ratings)
    Performance
    6.4
    (1 ratings)
    Support Rating
    4.8
    (11 ratings)
    Configurability
    6.4
    (1 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    Ease of integration
    7.3
    (1 ratings)
    Product Scalability
    7.3
    (1 ratings)
    Professional Services
    8.2
    (2 ratings)
    User Testimonials
    Google BigQuery
    Likelihood to Recommend
    Google
    Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
    Incentivized
    Read full review
    Pros
    Google
    • Realtime integration with Google Sheets.
    • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
    • Seamless integration with other GCP products.
    • A simple pipeline might look like this:-
    • GForms -> GSheets -> BigQuery -> Looker
    • It all links up really well and with ease.
    • One instance holds many projects.
    • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
    Incentivized
    Read full review
    Cons
    Google
    • 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.
    Incentivized
    Read full review
    Likelihood to Renew
    Google
    We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
    Incentivized
    Read full review
    Usability
    Google
    I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
    Incentivized
    Read full review
    Reliability and Availability
    Google
    I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
    Incentivized
    Read full review
    Performance
    Google
    I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
    Incentivized
    Read full review
    Support Rating
    Google
    BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
    Incentivized
    Read full review
    Alternatives Considered
    Google
    PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
    Incentivized
    Read full review
    Contract Terms and Pricing Model
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Scalability
    Google
    We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
    Incentivized
    Read full review
    Professional Services
    Google
    Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
    Read full review
    Return on Investment
    Google
    • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
    • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
    • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
    Incentivized
    Read full review
    ScreenShots

    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.