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Google BigQuery vs. IBM Watson Studio on Cloud Pak for Data

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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)

    IBM Watson Studio

    Score10 out of 10
    N/AIBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.N/A
    Pricing
    Google BigQueryIBM Watson Studio
    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 BigQueryIBM Watson Studio
    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 BigQueryIBM Watson Studio
    Considered Both Products
    Google
    No answer on this topic
    IBM
    Chose IBM Watson Studio
    • Data ingestion
    • Batch data processing
    • Built-in connectors to Python
    Incentivized
    Chose IBM Watson Studio
    IBM DSx lost the comparison.
    Incentivized
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    100%
    Would buy again
    5 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    No answers on this topic
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    100%
    Happy with the feature set
    5 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    No answers on this topic
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    No answers on this topic
    Features
    Google BigQueryIBM Watson Studio
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and IBM Watson Studio on Cloud Pak for Data
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    IBM Watson Studio on Cloud Pak for Data
    -
    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 Connectivity
    Comparison of Platform Connectivity features of Google BigQuery and IBM Watson Studio on Cloud Pak for Data
    Feature
    Google BigQuery
    -
    Ratings
    IBM Watson Studio on Cloud Pak for Data
    8.1
    22 Ratings
    3% below category average
    Connect to Multiple Data Sources00 Ratings8.022 Ratings
    Extend Existing Data Sources00 Ratings8.022 Ratings
    Automatic Data Format Detection00 Ratings10.021 Ratings
    MDM Integration00 Ratings6.414 Ratings
    Data Exploration
    Comparison of Data Exploration features of Google BigQuery and IBM Watson Studio on Cloud Pak for Data
    Feature
    Google BigQuery
    -
    Ratings
    IBM Watson Studio on Cloud Pak for Data
    10.0
    22 Ratings
    17% above category average
    Visualization00 Ratings10.022 Ratings
    Interactive Data Analysis00 Ratings10.022 Ratings
    Data Preparation
    Comparison of Data Preparation features of Google BigQuery and IBM Watson Studio on Cloud Pak for Data
    Feature
    Google BigQuery
    -
    Ratings
    IBM Watson Studio on Cloud Pak for Data
    9.5
    22 Ratings
    15% above category average
    Interactive Data Cleaning and Enrichment00 Ratings10.022 Ratings
    Data Transformations00 Ratings10.021 Ratings
    Data Encryption00 Ratings8.020 Ratings
    Built-in Processors00 Ratings10.021 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of Google BigQuery and IBM Watson Studio on Cloud Pak for Data
    Feature
    Google BigQuery
    -
    Ratings
    IBM Watson Studio on Cloud Pak for Data
    9.5
    22 Ratings
    12% above category average
    Multiple Model Development Languages and Tools00 Ratings10.021 Ratings
    Automated Machine Learning00 Ratings10.022 Ratings
    Single platform for multiple model development00 Ratings10.022 Ratings
    Self-Service Model Delivery00 Ratings8.020 Ratings
    Model Deployment
    Comparison of Model Deployment features of Google BigQuery and IBM Watson Studio on Cloud Pak for Data
    Feature
    Google BigQuery
    -
    Ratings
    IBM Watson Studio on Cloud Pak for Data
    8.0
    22 Ratings
    6% below category average
    Flexible Model Publishing Options00 Ratings9.022 Ratings
    Security, Governance, and Cost Controls00 Ratings7.022 Ratings
    Best Alternatives
    Google BigQueryIBM Watson Studio
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    RapidMiner
    Score8.9 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Anaconda
    Score8.8 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    Posit
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryIBM Watson Studio
    Likelihood to Recommend
    9.0
    (79 ratings)
    8.0
    (65 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    8.2
    (1 ratings)
    Usability
    6.6
    (6 ratings)
    9.6
    (2 ratings)
    Availability
    7.3
    (1 ratings)
    8.2
    (1 ratings)
    Performance
    6.4
    (1 ratings)
    8.2
    (1 ratings)
    Support Rating
    4.8
    (11 ratings)
    8.2
    (1 ratings)
    In-Person Training
    -
    (0 ratings)
    8.2
    (1 ratings)
    Online Training
    -
    (0 ratings)
    8.2
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    7.3
    (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)
    8.2
    (1 ratings)
    Professional Services
    8.2
    (2 ratings)
    -
    (0 ratings)
    Vendor post-sale
    -
    (0 ratings)
    7.3
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    8.2
    (1 ratings)
    User Testimonials
    Google BigQueryIBM Watson Studio
    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
    IBM
    It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
    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
    IBM
    • Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
    • SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
    • Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
    • Estimator validation lets data scientists test and prove different models.
    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
    IBM
    • The cost is steep and so only companies with resources can afford it
    • It will be nice to have Chinese versions so that Chinese engineers can also use it easily
    • It takes a while to learn how to input different kinds of skin defects for detection
    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
    IBM
    because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
    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
    IBM
    The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
    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
    IBM
    From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
    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
    IBM
    Never had slow response even on our very busy network
    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
    IBM
    I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
    Incentivized
    Read full review
    In-Person Training
    Google
    No answers on this topic
    IBM
    The trainers on the job are very smart with solutions and very able in teaching
    Incentivized
    Read full review
    Online Training
    Google
    No answers on this topic
    IBM
    The Platform is very handy and suggests further steps according my previous interests
    Incentivized
    Read full review
    Implementation Rating
    Google
    No answers on this topic
    IBM
    It surprised us with unpredictable case of use and brand new points of view
    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
    IBM
    The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
    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
    IBM
    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
    IBM
    It helped us in getting from 0 to DSX without getting lost
    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
    IBM
    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
    IBM
    • Could instantly show data driven insights to drive 20% incremental revenue over existing results
    • Still don't have a real use case for unstructured data like twitter feed
    • Some of the insights around user actions have driven new projects to automate mundane tasks
    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.