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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)

    Treasure AI

    Score9 out of 10
    Mid-Size Companies (51-1,000 employees)
    Treasure AI is an enterprise customer data platform (CDP) that reclaims customer-centricity in the age of the digital customer. It does this by connecting all data and uniting teams and systems into one customer data platform to power purposeful engagements.N/A
    Pricing
    Google BigQueryTreasure AI
    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 BigQueryTreasure AI
    Free Trial
    YesNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeOptional
    Additional Details——
    More Pricing Information
    Community Pulse
    Google BigQueryTreasure AI
    Considered Both Products
    Google
    Chose Google BigQuery
    Treasure Data is more for the marketer rather than a developer audience, so depending on who your main users will be for the machine learning you can decide which tool is better. In our case we went with Treasure Data because it was more for a marketer and less for the …
    Incentivized
    Treasure AI
    Chose Treasure AI
    In terms of query speed and performance, Google BigQuery and Snowflake offer better performance at a lower cost. BigQuery's pricing on just the data scanned rather than cost of computation is far more attractive than Treasure Data's current model. We've selected Treasure Data …
    Incentivized
    Chose Treasure AI
    While Google BigQuery is an excellent data warehouse, it does not have all of the functionality of Treasure Data. Treasure Data's components make unifying and activating segments much easier.
    Incentivized
    Chose Treasure AI
    There is a limited amount of human resource in the market who has knowledge in CDP. Treasure Data is simple and easy to navigate so that a newbie might find it easy to grasp its working concepts and initiate performing on the same. Whereas Tealium is more suited for a person …
    Incentivized
    Chose Treasure AI
    - Treasure Data can handle much bigger dataset than Redshift
    - Bigquery provides a much better experience and scales much better
    Incentivized
    Chose Treasure AI
    We did not go real far down the path of evaluation of BigQuery.
    Incentivized
    Chose Treasure AI
    TD can definitely use some one of functionality like adding incremental data feature or creating store procedures
    Incentivized
    Chose Treasure AI
    In Treasure Data, everything is managed. While in the other products, we need to set up and maintain it ourselves. None of them provide an all-in-one data platform like Treasure Data.
    Incentivized
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    100%
    Would buy again
    10 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    100%
    Delivers good value for the price
    9 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
    8 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    100%
    Implementation went as expected
    9 Answers
    Features
    Google BigQueryTreasure AI
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Treasure AI
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Treasure AI
    -
    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 BigQueryTreasure AI
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    Twilio Segment
    Score8.4 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Adobe Real-Time CDP
    Score8.3 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    Tealium Customer Data Hub
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryTreasure AI
    Likelihood to Recommend
    9.0
    (79 ratings)
    9.0
    (89 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    9.1
    (5 ratings)
    Usability
    6.6
    (6 ratings)
    8.0
    (4 ratings)
    Availability
    7.3
    (1 ratings)
    9.1
    (1 ratings)
    Performance
    6.4
    (1 ratings)
    8.2
    (1 ratings)
    Support Rating
    4.8
    (11 ratings)
    8.2
    (7 ratings)
    In-Person Training
    -
    (0 ratings)
    6.4
    (1 ratings)
    Online Training
    -
    (0 ratings)
    7.3
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    6.4
    (2 ratings)
    Configurability
    6.4
    (1 ratings)
    7.3
    (1 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    -
    (0 ratings)
    Ease of integration
    7.3
    (1 ratings)
    9.1
    (1 ratings)
    Product Scalability
    7.3
    (1 ratings)
    9.1
    (1 ratings)
    Professional Services
    8.2
    (2 ratings)
    -
    (0 ratings)
    Vendor post-sale
    -
    (0 ratings)
    7.3
    (2 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    7.4
    (2 ratings)
    User Testimonials
    Google BigQueryTreasure AI
    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
    Treasure AI
    Treasure Data is well suited to integrating multiple data sources, including online and digital sources. It is also well suited to trigger audience activations to known customers based on their online activity, integrating 3rd party data, and activating target audiences to ad platforms.
    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
    Treasure AI
    • CDP provides a unified view of data from all touchpoints in the customer journey until a single customer uses the service. This feature is very helpful in making service decisions and direction.
    • It provides a variety of extensions to bring your data together in one place and helps you do this easily.
    • Kits provided by Treasure Box provide basic but helpful methods for further development of 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
    Treasure AI
    • Documentation is not always fully update --> better off reaching to support for some topics that are not covered
    • Small bugs on the graphical user interface
    • If 2 people are editing on the same project simultaneously, the latter that saves the workflow overwrites the changes of the former one
    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
    Treasure AI
    I do think that we definitely will be renewing. We are putting major resources, time, and effort into Treasure Data becoming an extension of our organization, in many ways. We are working toward complete synergies with this product and leadership is very excited about the direction we are heading to be completely customer-centric.
    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
    Treasure AI
    It's a easy platform to use and give the user detailed logs about what is going on in the workflows, so someone that do not have a lot of experience can start to work with it. And also the master segment usability is awesome, as we can filter a lot of data the way we want.
    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
    Treasure AI
    As treasure data has a 24 hours support, every time we has big issues that impacts the zones, we do have immediatly support from the treasure data team, so I would say that we do not have any issues with availability
    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
    Treasure AI
    Since treasure data has started having a huge amount of data, sometimes we do have problems with the workflows logs because we generate a lot of then. But with integrations I have not to complain, its really easy to integrate with other platforms.
    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
    Treasure AI
    The technical team has a good hold on the nuances of the data related to our organization. I have found the online technical support on their site quite responsive including the L1 support. In cases where the L1 team isn't able to resolve, I have found they are prompt in getting the product team's input to get a quick resolution.
    Incentivized
    Read full review
    In-Person Training
    Google
    No answers on this topic
    Treasure AI
    I was not here when treasure data was implemented to our company.
    Incentivized
    Read full review
    Online Training
    Google
    No answers on this topic
    Treasure AI
    I wasnt here at the training in the start, but I had a few training with treasure data for a few functionalities, and they provided me god explanations and great documentations, eve if the project were in beta.
    Incentivized
    Read full review
    Implementation Rating
    Google
    No answers on this topic
    Treasure AI
    Implementation was quick and our developers had very few issues with the SDK.
    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
    Treasure AI
    We chose Treasure Data for the supreme customer service and lack of hidden costs. We don't need to manage any infrastructure or scale anything to meet customer demand. Treasure Data handles everything and makes it easy for us to integrate and focus on the tasks at hand. There may be cheaper options but we do not regret our decision to go with Treasure Data one bit.
    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
    Treasure AI
    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
    Treasure AI
    In abi we do have a lot of data coming every day, so treasure data always give us god solutions and options that would fix the problem.
    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
    Treasure AI
    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
    Treasure AI
    • We have built and supported our source of truth data tables using Treasure. This forms the foundation of our decision making.
    • Most of our Tableau data sources are created using a Treasure Data export which is executed by workflows on a daily basis which allows us to have visibility into day to day performance and communicate them to a wide variety of roles.
    • We load custom data into our Salesforce instance which allows us to trigger certain workflows and build accountability - i.e. a "Sale" will only count once a certain product driven event occurs which comes from data we pipe into Treasure and then into Salesforce.
    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.

    Treasure AI Screenshots

    Screenshot of some of the out of the box integrations across advertising, CRM, databases, eCommerce, machine learning and more.Screenshot of the query toolsScreenshot of the audience builder