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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)

    SAP Datasphere

    Score8.6 out of 10
    N/ASAP Datasphere, the next generation of SAP Data Warehouse Cloud, is a comprehensive data service that enables data professionals to deliver seamless and scalable access to mission-critical business data. It provides a unified experience for data integration, data cataloging, semantic modeling, data warehousing, data federation, and data virtualization. SAP Datasphere enables users to distribute mission-critical business data — with business context and logic preserved — across the data…N/A
    Pricing
    Google BigQuerySAP Datasphere
    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 BigQuerySAP Datasphere
    Free Trial
    YesYes
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—SAP Datasphere is available as a subscription or consumption-based model. The SAP Datasphere capacity unit (CU) offers an adaptable approach to pricing that enables any workload on any hyperscaler. The number of CUs required is determined by the unique workload, with the ability to tailor the combination of required services within SAP Datasphere utilizing a flexible tenant configuration. The services that contribute to CU consumption are the core application (compute and storage), data lake, BW bridge, data integration, and data catalog (crawling and storage).
    More Pricing Information
    Community Pulse
    Google BigQuerySAP Datasphere
    Considered Both Products
    Google
    No answer on this topic
    SAP
    Chose SAP Datasphere
    some features are better, other for e.g. big data analysis or ai cases not
    Incentivized
    Chose SAP Datasphere
    Both tools are fairly the same, but we mainly focused on SAP Data Warehouse Cloud since we had a lot of SAP services that were simple to integrate with each other.
    Incentivized
    Chose SAP Datasphere
    SAP Data Warehouse Cloud is flexible, highly integrated, and cost-effective compared to competitors, which has poor backend integration capability. In our organization, we prefer SAP Data Warehouse Cloud since it integrates with all external platforms we have in our system.
    Incentivized
    Chose SAP Datasphere
    Creation of a live model with SAP Data Warehouse Cloud is not necessary when live connection is getting utilized unlike on other related platform.
    Incentivized
    Chose SAP Datasphere
    Companies become resilient to change when they use data-driven insights and can leverage these insights to introduce new business models, enter new markets, or sustain competitive advantage. In order to achieve faster and better business outcomes, I recommend the use of SAP …
    Incentivized
    Chose SAP Datasphere
    It is super easy to perform database machine learning with [SAP] Data Warehouse Cloud unlike on other alternative applications. [SAP] Data Warehouse Cloud also has links for tutorials and documentation all over on all solutions.
    Incentivized
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    95%
    Would buy again
    121 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    95%
    Delivers good value for the price
    108 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    93%
    Happy with the feature set
    119 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    92%
    Lived up to sales and marketing promises
    99 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    89%
    Implementation went as expected
    103 Answers
    Features
    Google BigQuerySAP Datasphere
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and SAP Datasphere
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    SAP Datasphere
    -
    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 metrics8.775 Ratings00 Ratings
    Automatic host deployment8.013 Ratings00 Ratings
    Best Alternatives
    Google BigQuerySAP Datasphere
    Small Businesses
    MongoDB Atlas
    Score7.7 out of 10
    No answers on this topic
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    No answers on this topic
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    Talend Data Fabric
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQuerySAP Datasphere
    Likelihood to Recommend
    9.0
    (79 ratings)
    8.7
    (127 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    6.8
    (4 ratings)
    Usability
    6.6
    (6 ratings)
    8.4
    (69 ratings)
    Availability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Performance
    6.4
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    4.8
    (11 ratings)
    9.0
    (21 ratings)
    Configurability
    6.4
    (1 ratings)
    -
    (0 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    8.4
    (8 ratings)
    Ease of integration
    7.3
    (1 ratings)
    -
    (0 ratings)
    Product Scalability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Professional Services
    8.2
    (2 ratings)
    8.8
    (4 ratings)
    User Testimonials
    Google BigQuerySAP Datasphere
    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
    SAP
    SAP Datasphere is well suited for scalable cloud based data integration scenarios which also opens up the doors for AI driven insights which are much harder to achieve with on-prem data warehouses. Considering the licensing model of SAP Datasphere being based on consumption driven capacity units cost can be a big consideration for organizations with large volumes of data that can be a pre-requisite for data mining and AI use cases. So this can be a bottleneck or not so well adopted scenario for SAP Datasphere.
    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
    SAP
    • SAP Data Warehouse Cloud offers free trial for 90 days with free 128 GB of storage and 64 GB memory.
    • Availability of self-service data modeling and analytics on SAP Data Warehouse Cloud enables users to access and analyze data without getting support from the IT team.
    • Without zero coding while collecting, connecting, analyzing and modeling data, it saves us time and operational costs of partnering with external IT support experts.
    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
    SAP
    • Need to have a good understanding of the SAP ecosystem to implement and use it.
    • From a cost perspective it can be little bit on the expensive side for enterprises.
    • The platform is still new and hence more subjected to bugs. But support for it is always good from the SAP team.
    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
    SAP
    Datasphere can come with its own challenges which can feel like a mountain to get over. However, once one has an understanding of how the product works it becomes easier to use. Once the time is spent to get it setup and working correctly very little is needed to keep it running smoothly. Its a great tool it just takes time to learn.
    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
    SAP
    SAP has suggested that BW 7.5 will be sunset soon. Usability is good in SAP BW Modernization. Instead of going with other non-SAP tools, staying with SAP will be suitable for existing users. Across the board, many of them are moving to DataSphere.
    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
    SAP
    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
    SAP
    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
    Read full review
    SAP
    I would greatly acknowledge the services of Sap Data [warehouse Cloud] because we were struggling before its arrival where we used to get manual data connections and this used to consume a lot of time but after its use, we now are able to connect data easily saving a lot of time and finances.
    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
    SAP
    Each of these listed software has its own unique strength and capacity that scales well. SAP Datasphere on its end up against them with more suitability for large establishments with complex data ecosystems with scalability support. Also, it avails a pay-as-you-go pricing for users, and it is widely up for data quality, data governance, and data discovery.
    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
    SAP
    Despite the pricing model being expensive for small businesses, it provides decent features and capabilities for organizations of different sizes and it's an appropriate investment in today's business environment where there is constant pressure to build a scalable and flexible analytics service
    Incentivized
    Read full review
    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
    SAP
    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
    SAP
    Ever since we implemented SAP Data Warehouse Cloud, we have been able to reduce the additional costs of hiring third-party service providers by incorporating professional services offered by the vendor.
    Incentivized
    Read full review
    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
    SAP
    • Preserving data quality has enhanced governance on data by having a single source that is accessible to every business user via self-service capabilities.
    • Operational cost is lowered by connecting data in one integrated solution hence making it easy to access information without having to keeping logging to other applications. Additionally, no external IT support is needed since SAP Data Warehouse Cloud has no-coding modeling tools.
    • SAP Data Warehouse Cloud has enabled every business user to understand different data by transforming data to real insights.
    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.