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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)

    Tableau Desktop

    Score8.6 out of 10
    N/ATableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.

    $1,380

    per year (purchased via a Creator license)

    Pricing
    Google BigQueryTableau Desktop
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Tableau Creator License
    $115
    per month (billed annually) per user
    Offerings
    Pricing Offerings
    Google BigQueryTableau Desktop
    Free Trial
    YesNo
    Free/Freemium Version
    YesYes
    Premium Consulting/Integration Services
    NoYes
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—All pricing plans are billed annually. A Creator license includes Tableau Desktop, Tableau Prep Builder, and Tableau Pulse. Discounts sometimes available for volume.
    More Pricing Information
    Community Pulse
    Google BigQueryTableau Desktop
    Considered Both Products
    Google
    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
    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
    Google BigQuery works similarly to AWS. We ended up going with Google BigQuery due to contractual restrictions imposed by one of our customers.
    Incentivized
    Chose Google BigQuery
    At my previous organization we used server based SQL server. There were days when the server was down and we couldn't work or access the data. This caused multiple reports and processes which were fed from the server to fail. Google BigQuery doesn't have such problems.
    Incentivized
    Chose Google BigQuery
    I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. …
    Incentivized
    Chose Google BigQuery
    Other locally hosted solutions are capable of providing the required level of performance, but the administration requirements are significantly more involved than with BigQuery. Additionally, there are capacity and availability concerns with locally hosted platforms that are a …
    Incentivized
    Tableau
    Chose Tableau Desktop
    Looker has the benefit of being owned by Google and seamless interface with BigQuery. We didn't use BigQuery at the time, so the benefit wasn't realized. However, we are starting to use it more and more, and will be evaluating Looker again.

    The main drawback of Looker compared …
    Incentivized
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    93%
    Would buy again
    52 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    91%
    Delivers good value for the price
    50 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    98%
    Happy with the feature set
    55 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    93%
    Lived up to sales and marketing promises
    38 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    100%
    Implementation went as expected
    44 Answers
    Features
    Google BigQueryTableau Desktop
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Tableau Desktop
    -
    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
    BI Standard Reporting
    Comparison of BI Standard Reporting features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    -
    Ratings
    Tableau Desktop
    8.5
    175 Ratings
    4% above category average
    Pixel Perfect reports00 Ratings8.0145 Ratings
    Customizable dashboards00 Ratings9.2174 Ratings
    Report Formatting Templates00 Ratings8.1151 Ratings
    Ad-hoc Reporting
    Comparison of Ad-hoc Reporting features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    -
    Ratings
    Tableau Desktop
    8.5
    172 Ratings
    6% above category average
    Drill-down analysis00 Ratings8.6167 Ratings
    Formatting capabilities00 Ratings8.6170 Ratings
    Integration with R or other statistical packages00 Ratings8.1126 Ratings
    Report sharing and collaboration00 Ratings8.6165 Ratings
    Report Output and Scheduling
    Comparison of Report Output and Scheduling features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    -
    Ratings
    Tableau Desktop
    8.5
    166 Ratings
    3% above category average
    Publish to Web00 Ratings8.1155 Ratings
    Publish to PDF00 Ratings8.2154 Ratings
    Report Versioning00 Ratings8.6120 Ratings
    Report Delivery Scheduling00 Ratings8.6128 Ratings
    Delivery to Remote Servers00 Ratings8.978 Ratings
    Data Discovery and Visualization
    Comparison of Data Discovery and Visualization features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    -
    Ratings
    Tableau Desktop
    8.4
    164 Ratings
    5% above category average
    Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings8.7162 Ratings
    Location Analytics / Geographic Visualization00 Ratings8.6156 Ratings
    Predictive Analytics00 Ratings8.7131 Ratings
    Pattern Recognition and Data Mining00 Ratings7.87 Ratings
    Access Control and Security
    Comparison of Access Control and Security features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    -
    Ratings
    Tableau Desktop
    9.1
    149 Ratings
    7% above category average
    Multi-User Support (named login)00 Ratings9.1145 Ratings
    Role-Based Security Model00 Ratings9.1125 Ratings
    Multiple Access Permission Levels (Create, Read, Delete)00 Ratings8.9136 Ratings
    Report-Level Access Control00 Ratings9.110 Ratings
    Single Sign-On (SSO)00 Ratings9.383 Ratings
    Mobile Capabilities
    Comparison of Mobile Capabilities features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    -
    Ratings
    Tableau Desktop
    7.9
    141 Ratings
    2% above category average
    Responsive Design for Web Access00 Ratings8.6130 Ratings
    Mobile Application00 Ratings7.5101 Ratings
    Dashboard / Report / Visualization Interactivity on Mobile00 Ratings7.5122 Ratings
    Application Program Interfaces (APIs) / Embedding
    Comparison of Application Program Interfaces (APIs) / Embedding features of Google BigQuery and Tableau Desktop
    Feature
    Google BigQuery
    -
    Ratings
    Tableau Desktop
    7.7
    67 Ratings
    1% below category average
    REST API00 Ratings8.459 Ratings
    Javascript API00 Ratings7.653 Ratings
    iFrames00 Ratings6.751 Ratings
    Java API00 Ratings8.048 Ratings
    Themeable User Interface (UI)00 Ratings7.154 Ratings
    Customizable Platform (Open Source)00 Ratings8.448 Ratings
    Best Alternatives
    Google BigQueryTableau Desktop
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    Cyfe
    Score4 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Qlik Cloud Analytics (Qlik Sense)
    Score8.1 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    Sisense
    Score6.9 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryTableau Desktop
    Likelihood to Recommend
    9.0
    (79 ratings)
    9.1
    (204 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    7.5
    (41 ratings)
    Usability
    6.6
    (6 ratings)
    8.4
    (73 ratings)
    Availability
    7.3
    (1 ratings)
    10.0
    (11 ratings)
    Performance
    6.4
    (1 ratings)
    8.0
    (10 ratings)
    Support Rating
    4.8
    (11 ratings)
    1.0
    (57 ratings)
    In-Person Training
    -
    (0 ratings)
    9.4
    (4 ratings)
    Online Training
    -
    (0 ratings)
    8.0
    (5 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (34 ratings)
    Configurability
    6.4
    (1 ratings)
    7.0
    (3 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    -
    (0 ratings)
    Data Sharing and Collaboration
    -
    (0 ratings)
    8.4
    (115 ratings)
    Data Sources
    -
    (0 ratings)
    7.9
    (115 ratings)
    Ease of integration
    7.3
    (1 ratings)
    10.0
    (1 ratings)
    Product Scalability
    7.3
    (1 ratings)
    9.0
    (4 ratings)
    Professional Services
    8.2
    (2 ratings)
    -
    (0 ratings)
    Vendor post-sale
    -
    (0 ratings)
    10.0
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    10.0
    (1 ratings)
    User Testimonials
    Google BigQueryTableau Desktop
    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
    Tableau
    The best scenario is definitely to collect data from several sources and create dedicated dashboards for specific recipients. However, I miss the possibility of explaining these reports in more detail. Sometimes, we order a report, and after half a year, we don't remember the meaning of some data (I know it's our fault as an organization, but the tool could force better practices).
    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
    Tableau
    • An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
    • Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.
    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
    Tableau
    • Pricing should be more user-friendly and usage-driven
    • Making edits to the production reports is fairly tough and has a vast scope of additional capabilities
    • Tableau Desktop should be able to differentiate itself from the Tableau server else there is no major meaning of two different products being offered
    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
    Tableau
    Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.
    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
    Tableau
    Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.
    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
    Tableau
    When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.
    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
    Tableau
    Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast
    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
    Tableau
    Tableau support has been extremely responsive and willing to help with all of our requests. They have assisted with creating advanced analysis and many different types of custom icons, data formatting, formulas, and actions embedded into graphs. Tableau offers a weekly presentation of features and assists with internal company projects.
    Incentivized
    Read full review
    In-Person Training
    Google
    No answers on this topic
    Tableau
    It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.
    Read full review
    Online Training
    Google
    No answers on this topic
    Tableau
    I think the training was good overall, but it was maybe stating the obvious things that a tech savvy young engineer would be able to pick up themselves too. However, the example work books were good and Tableau web community has helped me with many problems
    Incentivized
    Read full review
    Implementation Rating
    Google
    No answers on this topic
    Tableau
    Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.
    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
    Tableau
    I have used Power BI as well, the pricing is better, and also training costs or certifications are not that high. Since there is python integration in Power BI where I can use data cleaning and visualizing libraries and also some machine learning models. I can import my python scripts and create a visualization on processed data.
    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
    Tableau
    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
    Tableau
    Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.
    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
    Tableau
    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
    Tableau
    • Tableau was acquired years ago, and has provided good value with the content created.
    • Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
    • Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.
    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.