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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)

    Google Cloud Storage

    Score9.2 out of 10
    N/AGoogle Cloud Storage is unified object storage for developers and enterprises.N/A
    Pricing
    Google BigQueryGoogle Cloud Storage
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    No answers on this topic
    Offerings
    Pricing Offerings
    Google BigQueryGoogle Cloud Storage
    Free Trial
    YesNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Google BigQueryGoogle Cloud Storage
    Considered Both Products
    Google
    Chose Google BigQuery
    Google BigQuery is widely used for structured data.
    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
    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
    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
    BigQuery is better at storing and handling large amounts of data than Knime. Knime is locally run and does not have the ability to handle massive databases like BigQuery and importing from multiple sources for multiple teams would be impossible, that is not really the function …
    Incentivized
    Chose Google BigQuery
    We liked BQ because the cost of it is only dependent on the amount of data you store (and there are tiers of data access) and how much you search. For us, it is significantly less expensive to run BQ than an equivalent hosted RDBMS. Because most of our data pipelines are …
    Incentivized
    Chose Google BigQuery
    BigQuery by far the best solution in all angles compared to other ones: Especially scalability, ease of use, performance and there is no need to manage any cluster of servers. Also it's ABSOLUTELY pay as you go! No one in market currently provide such service that can compete …
    Incentivized
    Chose Google BigQuery
    Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little …
    Incentivized
    Google
    Chose Google Cloud Storage
    The two services are very comparable, but we have many different services that all run on the Google Cloud Platform and therefore Google Cloud Storage made more sense as our storage solution rather than looking to an outside service like S3. Either one of these options would …
    Incentivized
    Chose Google Cloud Storage
    The big difference against other competitors that offer a Cloud storage solution, is the fact that Google Cloud Storage and other Google Cloud Platform products are billed in the local currency. This fact saves a lot of money because in some countries we have to pay for …
    Incentivized
    Chose Google Cloud Storage
    Aside from Google Cloud Storage, we've used AWS S3 and have found the two comparable. In fact, GCS is one of a large number of object storage systems that are compatible with S3 (including DigitalOcean Spaces, IBM Cloud Storage, and Azure Blog Storage). When it comes to these …
    Incentivized
    Chose Google Cloud Storage
    Google Cloud Storage is really a different application. It does not have the data parsing capabilities and it is simply a cold storage option in our use case. This means it is less flexible and has limited uses, but the uses it does have it, performs very well. There is a very …
    Incentivized
    Chose Google Cloud Storage
    We selected GCS vs. others because we decided to use other Google Cloud services. Since we integrated GCS into our tools, we're still using GCS today, even though we've largely transitioned away from Google Compute services. GCS is still a very solid choice, even if your server …
    Incentivized
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    100%
    Would buy again
    18 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    100%
    Delivers good value for the price
    17 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    100%
    Happy with the feature set
    18 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    100%
    Lived up to sales and marketing promises
    16 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    100%
    Implementation went as expected
    17 Answers
    Features
    Google BigQueryGoogle Cloud Storage
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Google Cloud Storage
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Google Cloud Storage
    -
    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
    Best Alternatives
    Google BigQueryGoogle Cloud Storage
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    Azure Blob Storage
    Score8.7 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Everpure FlashBlade
    Score9.9 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    Azure Blob Storage
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryGoogle Cloud Storage
    Likelihood to Recommend
    9.0
    (79 ratings)
    9.0
    (37 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    9.0
    (2 ratings)
    Usability
    6.6
    (6 ratings)
    9.0
    (14 ratings)
    Availability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Performance
    6.4
    (1 ratings)
    9.0
    (9 ratings)
    Support Rating
    4.8
    (11 ratings)
    7.8
    (13 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (1 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 BigQueryGoogle Cloud Storage
    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
    Google
    [Google Cloud Storage is] great for storing and playing large video files, and even sharing them securely with others, whether or not they are part of your organization. No need to download video files before watching, and can also be used to store any other kinds of files.
    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
    Google
    • Really great, easy to use interface helps us manage files easily. Storage is fast and inexpensive, so we don't have to spin up storage instances locally
    • Great set of command-line tools to manage data and storage options via scripts and apps, as well as an SDK means we can build GCS into our orchestration and operations tools
    • Robust integration with other Google cloud tools means that we don't have to think too hard about using GCS for a variety of storage tasks as we interact with other Google services.
    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
    Google
    • Currently can't delete folders which means there are cluttered folders on my cloud.
    • Easier upload options would be nice. The ability to upload and store other file types with an easier interface.
    • Managing files and storage could be improved a little for easier access and editing.
    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
    Google
    after all of the investment made in the tool and considering how many teams use it I think we would not be likely to move away from this tool. A lot of our information including historical is already here and we are happy with the capabilities of the tool currently
    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
    Google
    Very easy to use. I love having my data backed up. I love that Google Cloud Storage provides me with the peace of mind that I no longer need to worry about my data being lost. I can now sleep better at night. Google Cloud Storage is very easy to use. Overall, you save time and have less stress by using Google Cloud Storage.
    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
    Google
    No answers on this topic
    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
    Google
    For performance i give Google Cloud Storage 10 of 10 on performance because even though there are other softwares that do exactly the same thing as Google Drive, it still works exceptionally well. It is very fast, and and far as integration, the only software I have used with it that integrated was Google Docs, and of course it integrates perfectly.
    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
    Google
    We have never used official support from Google for our Google Cloud Storage, but there is plenty of documentation in place already. With a small amount of work, anybody should be able to get started. Once needs get more complicated, there is still documentation from Google, but also plenty of community support for common use cases around the internet.
    Incentivized
    Read full review
    Implementation Rating
    Google
    No answers on this topic
    Google
    overall I was not directly involved but hears the teams were satisfied with the implementation. the teams that used the tool did not encounter major issues, it was as expected with minor issues and bugs that were resolved later. The more significant learning curve was actually starting to use the tool
    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
    Google
    We prefer Google Cloud Storage over Amazon Web Services because of the tools and code integration offered by Google Cloud Storage. We found the Google Cloud Storage toolset to be highly usable and give us the fine-grained control we need for managing digital assets. Ultimately, we chose Google Cloud Storage because we found the API and suitability for code integration with our Java codebase to be impeccable and because we had excellent direct support from the Google Cloud Storage team
    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
    Google
    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
    Google
    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
    Google
    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
    Google
    • It has assisted greatly with our ability to share documents/information cross functionally. Especially within our advertising team, we store a large amount of information to assist new hires and refresh current employees.
    • Something that could improve is employees' understanding of how to best utilize Google Cloud Storage. This could improve by implementing a potential training video or tutorial.
    • Overall, Google Storage has been great. I have not used a similar storage product that had the same enterprise level capabilities.
    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.