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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)

    Microsoft Azure

    Score8.5 out of 10
    N/AMicrosoft Azure is a cloud computing platform and infrastructure for building, deploying, and managing applications and services through a global network of Microsoft-managed datacenters.

    $29

    per month

    Pricing
    Google BigQueryMicrosoft Azure
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Developer
    $29
    per month
    Standard
    $100
    per month
    Professional Direct
    $1000
    per month
    Basic
    Free
    per month
    Offerings
    Pricing Offerings
    Google BigQueryMicrosoft Azure
    Free Trial
    YesYes
    Free/Freemium Version
    YesYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—The free tier lets users have access to a variety of services free for 12 months with limited usage after making an Azure account.
    More Pricing Information
    Community Pulse
    Google BigQueryMicrosoft Azure
    Considered Both Products
    Google
    No answer on this topic
    Microsoft
    No answer on this topic
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    92%
    Would buy again
    34 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    94%
    Delivers good value for the price
    31 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    95%
    Happy with the feature set
    35 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    89%
    Lived up to sales and marketing promises
    24 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    94%
    Implementation went as expected
    34 Answers
    Features
    Google BigQueryMicrosoft Azure
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Microsoft Azure
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Microsoft Azure
    -
    Ratings
    Automatic software patching8.017 Ratings00 Ratings
    Database scalability9.079 Ratings00 Ratings
    Automated backups8.524 Ratings00 Ratings
    Database security provisions8.873 Ratings00 Ratings
    Monitoring and metrics8.675 Ratings00 Ratings
    Automatic host deployment8.013 Ratings00 Ratings
    Infrastructure-as-a-Service (IaaS)
    Comparison of Infrastructure-as-a-Service (IaaS) features of Google BigQuery and Microsoft Azure
    Feature
    Google BigQuery
    -
    Ratings
    Microsoft Azure
    8.4
    27 Ratings
    2% above category average
    Service-level Agreement (SLA) uptime00 Ratings8.126 Ratings
    Dynamic scaling00 Ratings8.725 Ratings
    Elastic load balancing00 Ratings8.624 Ratings
    Pre-configured templates00 Ratings8.225 Ratings
    Monitoring tools00 Ratings8.326 Ratings
    Pre-defined machine images00 Ratings8.424 Ratings
    Operating system support00 Ratings8.926 Ratings
    Security controls00 Ratings8.626 Ratings
    Automation00 Ratings8.224 Ratings
    Best Alternatives
    Google BigQueryMicrosoft Azure
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    IBM Cloud Object Storage
    Score9 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    IBM Cloud Bare Metal Servers
    Score8.7 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    SAP on IBM Cloud
    Score9 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryMicrosoft Azure
    Likelihood to Recommend
    9.0
    (79 ratings)
    8.7
    (96 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    10.0
    (17 ratings)
    Usability
    6.6
    (6 ratings)
    8.3
    (36 ratings)
    Availability
    7.3
    (1 ratings)
    6.8
    (2 ratings)
    Performance
    6.4
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    4.8
    (11 ratings)
    9.0
    (27 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (2 ratings)
    Configurability
    6.4
    (1 ratings)
    -
    (0 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    -
    (0 ratings)
    Ease of integration
    7.3
    (1 ratings)
    -
    (0 ratings)
    Product Scalability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Professional Services
    8.2
    (2 ratings)
    -
    (0 ratings)
    User Testimonials
    Google BigQueryMicrosoft Azure
    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
    Microsoft
    Azure is particularly well suited for enterprise environments with existing Microsoft investments, those that require robust compliance features, and organizations that need hybrid cloud capabilities that bridge on-premises and cloud infrastructure. In my opinion, Azure is less appropriate for cost-sensitive startups or small businesses without dedicated cloud expertise and scenarios requiring edge computing use cases with limited connectivity. Azure offers comprehensive solutions for most business needs but can feel like there is a higher learning curve than other cloud-based providers, depending on the product and use case.
    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
    Microsoft
    • Microsoft Azure is highly scalable and flexible. You can quickly scale up or down additional resources and computing power.
    • You have no longer upfront investments for hardware. You only pay for the use of your computing power, storage space, or services.
    • The uptime that can be achieved and guaranteed is very important for our company. This includes the rapid maintenance for security updates that are mostly carried out by Microsoft.
    • The wide range of capabilities of services that are possible in Microsoft Azure. You can practically put or create anything in Microsoft Azure.
    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
    Microsoft
    • The cost of resources is difficult to determine, technical documentation is frequently out of date, and documentation and mapping capabilities are lacking.
    • The documentation needs to be improved, and some advanced configuration options require research and experimentation.
    • Microsoft's licensing scheme is too complex for the average user, and Azure SQL syntax is too different from traditional SQL.
    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
    Microsoft
    Moving to Azure was and still is an organizational strategy and not simply changing vendors. Our product roadmap revolved around Azure as we are in the business of humanitarian relief and Azure and Microsoft play an important part in quickly and efficiently serving all of the world. Migration and investment in Azure should be considered as an overall strategy of an organization and communicated companywide.
    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
    Microsoft
    As Microsoft Azure is [doing a] really good with PaaS. The need of a market is to have [a] combo of PaaS and IaaS. While AWS is making [an] exceptionally well blend of both of them, Azure needs to work more on DevOps and Automation stuff. Apart from that, I would recommend Azure as a great platform for cloud services as scale.
    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
    Microsoft
    It has proven to be unreliable in our production environment and services become unavailable without proper notification to system administrators
    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
    Microsoft
    No answers on this topic
    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
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    Microsoft
    We were running Windows Server and Active Directory, so [Microsoft] Azure was a seamless transition. We ran into a few, if any support issues, however, the availability of Microsoft Azure's support team was more than willing and able to guide us through the process. They even proposed solutions to issues we had not even thought of!
    Incentivized
    Read full review
    Implementation Rating
    Google
    No answers on this topic
    Microsoft
    As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.
    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
    Microsoft
    As I continue to evaluate the "big three" cloud providers for our clients, I make the following distinctions, though this gap continues to close. AWS is more granular, and inherently powerful in the configuration options compared to [Microsoft] Azure. It is a "developer" platform for cloud. However, Azure PowerShell is helping close this gap. Google Cloud is the leading containerization platform, largely thanks to it building kubernetes from the ground up. Azure containerization is getting better at having the same storage/deployment options.
    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
    Microsoft
    No answers on this topic
    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
    Microsoft
    No answers on this topic
    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
    Microsoft
    No answers on this topic
    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
    Microsoft
    • For about 2 years we didn't have to do anything with our production VMs, the system ran without a hitch, which meant our engineers could focus on features rather than infrastructure.
    • DNS management was very easy in Azure, which made it easy to upgrade our cluster with zero downtime.
    • Azure Web UI was easy to work with and navigate, which meant our senior engineers and DevOps team could work with Azure without formal training.
    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.