TrustRadius: an HG Insights company

Save this comparison

Save this comparison

Add Product

Recommended Comparisons

    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)

    Pega Platform

    Score8.5 out of 10
    N/APega Platform is a combined business process management and robotic process automation (RPA) platform with advanced workforce analytics from Pegasystems.

    $0.45

    one-time fee per case**

    Pricing
    Google BigQueryPega Platform
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Low-code Factory Edition
    $0.45
    one-time fee per case**
    Standard Edition
    $0.80
    one-time fee per case**
    Enterprise Edition
    Custom Quote
    Offerings
    Pricing Offerings
    Google BigQueryPega Platform
    Free Trial
    YesYes
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—**350,000 cases / year minimum. Additional cases available in blocks of 150,000.
    More Pricing Information
    Community Pulse
    Google BigQueryPega Platform
    Considered Both Products
    Google
    No answer on this topic
    Pegasystems
    No answer on this topic
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    90%
    Would buy again
    9 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    100%
    Delivers good value for the price
    8 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    100%
    Happy with the feature set
    10 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
    5 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    78%
    Implementation went as expected
    7 Answers
    Features
    Google BigQueryPega Platform
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Pega Platform
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Pega Platform
    -
    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
    Reporting & Analytics
    Comparison of Reporting & Analytics features of Google BigQuery and Pega Platform
    Feature
    Google BigQuery
    -
    Ratings
    Pega Platform
    5.3
    63 Ratings
    37% below category average
    Dashboards00 Ratings4.062 Ratings
    Standard reports00 Ratings6.062 Ratings
    Custom reports00 Ratings6.061 Ratings
    Process Engine
    Comparison of Process Engine features of Google BigQuery and Pega Platform
    Feature
    Google BigQuery
    -
    Ratings
    Pega Platform
    8.0
    66 Ratings
    4% below category average
    Process designer00 Ratings9.065 Ratings
    Process simulation00 Ratings7.957 Ratings
    Business rules engine00 Ratings10.065 Ratings
    SOA support00 Ratings7.151 Ratings
    Process player00 Ratings7.048 Ratings
    Support for modeling languages00 Ratings5.46 Ratings
    Form builder00 Ratings9.059 Ratings
    Model execution00 Ratings8.956 Ratings
    Collaboration
    Comparison of Collaboration features of Google BigQuery and Pega Platform
    Feature
    Google BigQuery
    -
    Ratings
    Pega Platform
    9.0
    50 Ratings
    8% above category average
    Social collaboration tools00 Ratings9.050 Ratings
    Content Management Capabilties
    Comparison of Content Management Capabilties features of Google BigQuery and Pega Platform
    Feature
    Google BigQuery
    -
    Ratings
    Pega Platform
    4.4
    9 Ratings
    59% below category average
    Content management00 Ratings4.49 Ratings
    Best Alternatives
    Google BigQueryPega Platform
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    Process Street
    Score8.2 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    TIBCO® BPM Enterprise
    Score8 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    IBM Business Automation Workflow
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryPega Platform
    Likelihood to Recommend
    9.0
    (79 ratings)
    7.9
    (74 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    10.0
    (7 ratings)
    Usability
    6.6
    (6 ratings)
    9.0
    (2 ratings)
    Availability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Performance
    6.4
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    4.8
    (11 ratings)
    10.0
    (7 ratings)
    Online Training
    -
    (0 ratings)
    8.0
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    10.0
    (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 BigQueryPega Platform
    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
    Pegasystems
    Pega Platform has introduced the low code feature, using app studio seasonal and professional developer can develop application easily and quickly. The initial application can be build by Business analyst and product owner who has less knowledge of Pega Platform, further application can be enhanced/extended by professional developer. We can develop end to end application and promote to higher environment. Easily we can perform parallel development using branch.
    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
    Pegasystems
    • Quick development time. Much of the Pega "rules" are easy to configure and implement.
    • Visually friendly and modern. Much of the UI/UX elements in the system are continuously supported and updated, giving a nice feel to the apps.
    • Many of the configurations and rules Pega gives to the developers can also be delegated to business users. The organization and structure of the client's business can easily be adapted in the Pega platform.
    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
    Pegasystems
    • Need more learning materials. For Beginners who have previous programming experience with another language takes more time to learn.
    • If the developer is met with an issue in pega platform , they have to rely on Pega supporting team to fix it.
    • Customization is available but is not encouraged. Pega does not support faults that occur in customized solutions.
    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
    Pegasystems
    Pegasystems has continued to demonstrate a strong partnership with our organization and investment in their product that aligns with our overall vision and need. Pegasystems has engaged us at every level, with the assistance of minor defects to the overall roadmap planning and alignment of our goals
    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
    Pegasystems
    Pega Platform is enhancing its product and launching new features day by day which help to achieve customers needs. If I talk about the earlier version of Pega Platform (i.e. pega v5 and 6.3) there were many numbers of limitations in Pega Platform and if we need to do some customization then needed to write custom java and jave scripts to achieve the functionally. Now I can say Pega Platform is running with market trends and demand. Pega Platform is giving all the options which support the current technologies like decisioning capabilities, real time processing, mashup, process fabrics etc..
    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
    Pegasystems
    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
    Pegasystems
    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
    Pegasystems
    It’s very slow sometimes, but that may be our servers. Also the Knowledge Library needs some work - again, not sure if it’s our setup or what- but I’m unable to search the body of an article for content, so I have to be very intentional with tagging, but it’s not ideal.
    Incentivized
    Read full review
    Online Training
    Google
    No answers on this topic
    Pegasystems
    The online training is an excellent one, but still it is missing hands on development.
    Read full review
    Implementation Rating
    Google
    No answers on this topic
    Pegasystems
    Implementation is totally depend up on the requirement
    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
    Pegasystems
    We did evaluate multiple products offerings with Pega Platform capabilities and observed that Pega PRPC rules engine and case management capabilities are better over so many BPM Tools. We also conducted a detailed study with developers to identify the best products out of Suite of BPM products. It's observed that Rules engines integration is very streamlined with forms in Pega whereas other tools multiple have powerful data model capabilities but lacks the ease of creating business rules.
    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
    Pegasystems
    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
    Pegasystems
    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
    Pegasystems
    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
    Pegasystems
    • For one of the applications we worked on, we were able to reduce the processing time on a case from 2 days to 20 minutes by utilizing Pega
    • We were able to resolve the issue of the routing of cases to users based on a specific algorithm by use of Pega
    • Pega case management feature was extensively used in one of the application to establish a parent-child relationship which was very helpful for all the business users
    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.