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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 Vault

    Score8.6 out of 10
    N/AIBM Vault (formerly Hashicorp Vault) is an encryption tool for managing secrets including credentials, passwords and other secrets, providing access control, audit trail, and support for multiple authentication methods. It is available open source, or under an enterprise license.

    $0.03

    Pricing
    Google BigQueryIBM Vault
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Cloud - HCP Vault
    $0.03/hr
    Open Source
    Free
    Enterprise
    Contact sales team
    Offerings
    Pricing Offerings
    Google BigQueryIBM Vault
    Free Trial
    YesNo
    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 BigQueryIBM Vault
    Considered Both Products
    Google
    No answer on this topic
    IBM
    No answer on this topic
    Key User Insights
    Would buy again
    98%
    Would buy again
    64 Answers
    No answers on this topic
    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
    No answers on this topic
    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 Vault
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and IBM Vault
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    IBM Vault
    -
    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 BigQueryIBM Vault
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    Keeper
    Score8.3 out of 10
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Keeper
    Score8.3 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    No answers on this topic
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryIBM Vault
    Likelihood to Recommend
    9.0
    (79 ratings)
    8.0
    (7 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    10.0
    (1 ratings)
    Usability
    6.6
    (6 ratings)
    9.0
    (3 ratings)
    Availability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Performance
    6.4
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    4.8
    (11 ratings)
    6.3
    (3 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 BigQueryIBM Vault
    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
    HashiCorp Vault, in my opinion, is a defacto standard for any cloud or automation implementation. They're the best of the best as far as products for secrets management and the ability to use it against relatively any service you have is unheard of for other products. HashiCorp has really taken out all the stops when it comes to creating a nice, extensible tool that people can use to suit their 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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    IBM
    • The HTTP API you use to write and read secrets is open and can be used by any application.
    • It keeps our sensitive data/credentials out of our GitLab repositories.
    • Sealing and unsealing the Vault on demand adds an additional layer of security.
    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
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    IBM
    • Session Management is terrible to manage
    • Monitoring is hard and not enough information
    • User management
    • Configuration is too complex
    • More user friendly UI
    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
    HashiCorp Vault is the best there is out there, and it has become critical to our secret management use cases. It would be difficult to find anything that would suit our needs better and that would be beneficial for us to switch over to.
    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
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    IBM
    We spent a little more time than we imagined to conceptually understand how HashiCorp Vault operates, as well as how it is configured. This is not trivial, and keep in mind that you will need to take some time to get a thorough understanding of the tool. The documentation could be more helpful in this regard.
    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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    IBM
    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
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    IBM
    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
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    IBM
    Hashicorp has been very responsive to our questions and inquiries up to this point. We are currently working on them to develop a more granular permissions model within Vault. We are very close to achieving our objectives with the help of their support team. We do not seem to be in the same time zone which makes it hard for escalated issues.
    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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    IBM
    HashiCorp Vault is way better than Azure Key Vault; it has more features and it goes beyond a key-value secret store.
    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
    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
    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
    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
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    IBM
    • Helped us reach our security compliance goals.
    • Helped us strengthen our security position in our infrastructure by improving on poor secret management practices.
    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.

    IBM Vault Screenshots

    Screenshot of an example of writing a secret to Vault. Secrets are always encrypted and written to backend storage.Screenshot of the secrets menu to manage integrated secrets engines. Secrets Engines are components which store, generate, or encrypt data and are enabled at a path in Vault.Screenshot of where vault identity has support for groups. A group can contain multiple entities as its members. A group can also have subgroups.Screenshot of HCP Vault, which provides all of the power and security of Vault, without the complexity and overhead of managing it yourself.Screenshot of where to view entity client and non-entity client counts.Screenshot of MFA is built on top of the Identity system of Vault.