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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)

    Red Hat OpenShift

    Score9.1 out of 10
    N/AOpenShift is Red Hat's Cloud Computing Platform as a Service (PaaS) offering. OpenShift is an application platform in the cloud where application developers and teams can build, test, deploy, and run their applications.

    $0.08

    per hour

    Pricing
    Google BigQueryRed Hat OpenShift
    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 BigQueryRed Hat OpenShift
    Free Trial
    YesYes
    Free/Freemium Version
    YesYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Google BigQueryRed Hat OpenShift
    Considered Both Products
    Google
    No answer on this topic
    Red Hat
    No answer on this topic
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    97%
    Would buy again
    315 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    96%
    Delivers good value for the price
    268 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    99%
    Happy with the feature set
    322 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    97%
    Lived up to sales and marketing promises
    218 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    90%
    Implementation went as expected
    255 Answers
    Features
    Google BigQueryRed Hat OpenShift
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Red Hat OpenShift
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Red Hat OpenShift
    -
    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
    Platform-as-a-Service
    Comparison of Platform-as-a-Service features of Google BigQuery and Red Hat OpenShift
    Feature
    Google BigQuery
    -
    Ratings
    Red Hat OpenShift
    8.4
    324 Ratings
    8% above category average
    Ease of building user interfaces00 Ratings8.6275 Ratings
    Scalability00 Ratings9.3309 Ratings
    Platform management overhead00 Ratings8.3290 Ratings
    Workflow engine capability00 Ratings8.6262 Ratings
    Platform access control00 Ratings8.3291 Ratings
    Services-enabled integration00 Ratings8.5273 Ratings
    Development environment creation00 Ratings8.6283 Ratings
    Development environment replication00 Ratings8.3270 Ratings
    Issue monitoring and notification00 Ratings7.9284 Ratings
    Issue recovery00 Ratings8.1279 Ratings
    Upgrades and platform fixes00 Ratings8.5285 Ratings
    Best Alternatives
    Google BigQueryRed Hat OpenShift
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    IBM Cloud Foundry
    Score8.5 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    IBM Cloud Private
    Score9.6 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    AWS Elastic Beanstalk
    Score8.6 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryRed Hat OpenShift
    Likelihood to Recommend
    9.0
    (79 ratings)
    9.1
    (339 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    9.3
    (26 ratings)
    Usability
    6.6
    (6 ratings)
    8.5
    (10 ratings)
    Availability
    7.3
    (1 ratings)
    5.5
    (1 ratings)
    Performance
    6.4
    (1 ratings)
    9.0
    (125 ratings)
    Support Rating
    4.8
    (11 ratings)
    7.0
    (9 ratings)
    In-Person Training
    -
    (0 ratings)
    7.0
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    7.0
    (3 ratings)
    Configurability
    6.4
    (1 ratings)
    -
    (0 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    8.0
    (3 ratings)
    Ease of integration
    7.3
    (1 ratings)
    -
    (0 ratings)
    Product Scalability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Professional Services
    8.2
    (2 ratings)
    7.3
    (1 ratings)
    Vendor post-sale
    -
    (0 ratings)
    8.0
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    8.0
    (1 ratings)
    User Testimonials
    Google BigQueryRed Hat OpenShift
    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
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    Red Hat
    Red Hat OpenShift, despite its complexity and overhead, remains the most complete and enterprise-ready Kubernetes platform available. It excels in research projects like ours, where we need robust CI/CD, GPU scheduling, and tight integration with tools like Jupyter, OpenDataHub, and Quiskit. Its security, scalability, and operator ecosystem make it ideal for experimental and production-grade AI workloads. However, for simpler general hosting tasks—such as serving static websites or lightweight backend services—we find traditional VMs, Docker, or LXD more practical and resource-efficient. Red Hat OpenShift shines in complex, container-native workflows, but can be overkill for basic infrastructure needs.
    Incentivized
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    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
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    Red Hat
    • We had a few microservices that dealt with notifications and alerts. We used OpenShift to deploy these microservices, which handle and deliver notifications using publish-subscribe models.
    • We had to expose an API to consumers via MTLS, which was implemented using Server secret integration in OpenShift. We were then able to deploy the APIs on OpenShift with API security.
    • We integrated Splunk with OpenShift to view the logs of our applications and gain real-time insights into usage, as well as provide high availability.
    Incentivized
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    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
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    Red Hat
    • I wouldn't necessarily say there is look everyday technology transform. I can see a trend wherein Red Hat OpenShift is adopting all the new technology trends and helping their customers align with their priorities and the emerging technology trends. I wouldn't call out various scope for development every day. There is scope for development. It is all how the organizations adopt it and how they deliver it to their customers. I don't want to call out there is scope for development. It's happening. It is a never ending process.
    • At the moment, I don't have anything to call out. We are experiencing Red Hat OpenShift and we can see every day they're coming up with new features as and when they come up with new features, we want to experience it more and more. We are looking for opportunities wherein this can be leveraged to help our users and partners.
    Incentivized
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    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
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    Red Hat
    This is the current strategy for the company, most of the products in the organisation are aligning to Openshift and various use cases it support. Also lot of applications are being developed for AI use case, openshift.AI provides opportunity to host and leverage the AI capabilities for these applications
    Incentivized
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    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
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    Red Hat
    As I said before, the obserability is one of the weakest point of OpenShift and that has a lot to do with usability. The Kibana console is not fully integrated with OpenShift console and you have to switch from tab to tab to use it. Same with Prometheus, Jaeger and Grafan, it's a "simple" integration but if you want to do complex queries or dashboards you have to go to the specific console
    Incentivized
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    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
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    Red Hat
    Redhat openshift is generally reliable and available platform, it ensures high availability for most the situations. in fact the product where we put openshift in a box, we ensure that the availability is also happening at node and network level and also at storage level, so some of the factors that are outside of Openshift realm are also working in HA manner.
    Incentivized
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    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
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    Red Hat
    Overall, this platform is beneficial. The only downsides we have encountered have been with pods that occasionally hang. This results in resources being dedicated to dead or zombie pods. Over time, these wasted resources occasionally cause us issues, and we have had difficulty monitoring these pods. However, this issue does not overshadow the benefits we get from Openshift.
    Incentivized
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    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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    Red Hat
    Their customer support team is good and quick to respond. On a couple of occassions, they have helped us in solving some issues which we were finding a tad difficult to comprehend. On a rare occasion, the response was a bit slow but maybe it was because of the festival season. Overall a good experience on this front.
    Incentivized
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    In-Person Training
    Google
    No answers on this topic
    Red Hat
    I was not involved in the in person training, so i
    can not answer this question, but the team in my org worked directly
    with Openshift and able to get the in person training done easily, i did not
    hear problem or complain in this space, so i hope things happen
    seamlessly without any issue.
    Incentivized
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    Online Training
    Google
    No answers on this topic
    Red Hat
    We went thru the training material on RH webesite, i think its very descriptive and the handson lab sesssions are very useful. It would be good to create more short duration videos covering one single aspect of openshift, this wll keep the interest and also it breaks down the complexity to reasonable chunks.
    Incentivized
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    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
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    Red Hat
    The Tanzu Platform seemed overly complicated, and the frequent changes to the portfolio as well as the messaging made us uneasy. We also decided it would not be wise to tie our application platform to a specific infrastructure provider, as Tanzu cannot be deployed on anything other than vSphere. SUSE Rancher seemed good overall, but ultimately felt closer to a DIY approach versus the comprehensive package that Red Hat OpenShift provides.
    Incentivized
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    Contract Terms and Pricing Model
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Red Hat
    It's easy to understand what are being billed and what's included in each type of subscription. Same with the support (Std or Premium) you know exactly what to expect when you need to use it. The "core" unit approach on the subscription made really simple to scale and carry the workloads from one site to another.
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    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
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    Red Hat
    This is a great platform to deployment container applications designed for multiple use cases. Its reasonably scalable platform, that can host multiple instances of applications, which can seamlessly handle the node and pod failure, if they are configured properly. There should be some scalability best practices guide would be very useful
    Incentivized
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    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.
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    Red Hat
    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
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    Red Hat
    • That is a complicated question and one that's not easy for me to answer. There's a lot of factors that go into all of the stuff that we just don't have an easy way of measuring. And we realize that while we're implementing Red Hat OpenShift, we've tried to start measuring some of that stuff, but we don't have a baseline to go on. So it's hard to say. What I can tell you is general experience with the platform has been extremely positive from the development aspect. Teams have been very, very happy with the speed at which they're able to do stuff. They've been happy with that. The way it works in one environment is exactly the way it works in the next environment because we don't have configuration drift, that type of thing, and has had very positive impacts. But we didn't have a baseline to start with. So I can't talk about getting there faster or anything like that.
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    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.