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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 Security QRadar SIEM

    Score8.9 out of 10
    N/AIBM Security QRadar is security information and event management (SIEM) Software.N/A
    Pricing
    Google BigQueryIBM Security QRadar SIEM
    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 Security QRadar SIEM
    Free Trial
    YesYes
    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 Security QRadar SIEM
    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
    92%
    Would buy again
    67 Answers
    Delivers good value for the price
    97%
    Delivers good value for the price
    56 Answers
    97%
    Delivers good value for the price
    61 Answers
    Happy with the feature set
    97%
    Happy with the feature set
    63 Answers
    96%
    Happy with the feature set
    70 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    42 Answers
    86%
    Lived up to sales and marketing promises
    38 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    59 Answers
    94%
    Implementation went as expected
    59 Answers
    Features
    Google BigQueryIBM Security QRadar SIEM
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and IBM Security QRadar SIEM
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    IBM Security QRadar SIEM
    -
    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
    Security Information and Event Management (SIEM)
    Comparison of Security Information and Event Management (SIEM) features of Google BigQuery and IBM Security QRadar SIEM
    Feature
    Google BigQuery
    -
    Ratings
    IBM Security QRadar SIEM
    8.5
    69 Ratings
    7% above category average
    Centralized event and log data collection00 Ratings9.927 Ratings
    Correlation00 Ratings8.669 Ratings
    Event and log normalization/management00 Ratings9.527 Ratings
    Deployment flexibility00 Ratings7.827 Ratings
    Integration with Identity and Access Management Tools00 Ratings8.965 Ratings
    Custom dashboards and workspaces00 Ratings7.569 Ratings
    Host and network-based intrusion detection00 Ratings9.725 Ratings
    Data integration/API management00 Ratings9.07 Ratings
    Behavioral analytics and baselining00 Ratings7.648 Ratings
    Rules-based and algorithmic detection thresholds00 Ratings8.049 Ratings
    Response orchestration and automation00 Ratings7.75 Ratings
    Reporting and compliance management00 Ratings7.947 Ratings
    Incident indexing/searching00 Ratings8.97 Ratings
    Best Alternatives
    Google BigQueryIBM Security QRadar SIEM
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    No answers on this topic
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Sumo Logic
    Score8.9 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    SolarWinds Security Event Manager (SEM)
    Score8 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryIBM Security QRadar SIEM
    Likelihood to Recommend
    9.0
    (79 ratings)
    8.6
    (89 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    8.8
    (5 ratings)
    Usability
    6.6
    (6 ratings)
    8.0
    (2 ratings)
    Availability
    7.3
    (1 ratings)
    9.0
    (1 ratings)
    Performance
    6.4
    (1 ratings)
    9.0
    (1 ratings)
    Support Rating
    4.8
    (11 ratings)
    8.1
    (62 ratings)
    In-Person Training
    -
    (0 ratings)
    9.0
    (1 ratings)
    Online Training
    -
    (0 ratings)
    9.0
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (1 ratings)
    Configurability
    6.4
    (1 ratings)
    8.0
    (1 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    9.0
    (1 ratings)
    Ease of integration
    7.3
    (1 ratings)
    8.1
    (58 ratings)
    Product Scalability
    7.3
    (1 ratings)
    8.0
    (1 ratings)
    Professional Services
    8.2
    (2 ratings)
    10.0
    (1 ratings)
    Vendor post-sale
    -
    (0 ratings)
    9.0
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    9.0
    (1 ratings)
    User Testimonials
    Google BigQueryIBM Security QRadar SIEM
    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
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    IBM
    I would only recommend IBM Security QRadar SIEM in a few situations. For one, it's very easy to setup and use if all your log sources are generic from known vendors. It's also significantly cheaper than Splunk, which is nice if you're trying to save money or be more efficient. I would not recommend IBM Security QRadar SIEM for environments with a lot of custom logs and complicated detection requirements.
    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
    • Enables identification and prioritization of vulnerabilities in IT infrastructure for corrective action.
    • Facilitates security incident investigation and forensic analysis.
    • Provides a real-time view of security events, enabling immediate incident response.
    • Can integrate with external threat intelligence sources to enrich data and improve threat detection.
    • Enables the generation of detailed and customized reports.
    Incentivized
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    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
    • Need to spend more time configuring the system to properly interpret and normalize different type of data collected from multiple resources.
    • While Rule creation QRadar uses that rules to detect security threats and generate alerts, but to creating and managing rules is bit complex & tedious work to complete.
    • IBM Security QRadar SIEM is excellent in handling large & complex systems that requires in-depth knowledge and extensive training to configure and maintain the system which includes upgrading, optimization of performance & issue troubleshooting.
    Incentivized
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    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
    QRadar is an established and stable product, we have been using it for many years and want to continue to focus on it. Anyone who has used the product and knows it knows how reliable it is and how it facilitates continuous monitoring of threats from outside and inside. it is an exceptional product that is very useful for us.
    Incentivized
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    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
    As a grade I give 8 as QRadar is not easy to learn. It requires some time to master it. It also needs a team of people actively working on the product. Once you learn to use it the software works very well and it is easy to correlate and understand detected threats. It only takes time to learn how to use it well and configure it properly.
    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
    Customer support is Good of IBM, While Using IBM QRadar its deployment is to slow and suddenly stop working and crashed we have contacted IBM Support and Rised a Ticket within a few minute we get call back from customer support and Query Resolved by them Fast And Rapid Support of Ibm
    Incentivized
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    In-Person Training
    Google
    No answers on this topic
    IBM
    The training was very useful and the people who taught us were very knowledgeable. Although the software may initially seem difficult to learn they made things much easier for us.
    Incentivized
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    Online Training
    Google
    No answers on this topic
    IBM
    The training was very useful and the people who taught us were very knowledgeable. Although the software may initially seem difficult to learn they made things much easier for us.
    Incentivized
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    Implementation Rating
    Google
    No answers on this topic
    IBM
    Initial patience is required to learn how to use the product, and it takes a dedicated team to use it. One person is not enough, and it's not enough to just set it up and check it once in a while. It has to be used daily and kept under control to be used effectively
    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
    IBM Qradar takes the best from its competitors. Reliable and stable but sometimes very expensive, the SIEM from IBM offers a wide range of scenarios in which the customers can suite and size their own infrastructures. IBM Qradar doesn't really needs to stack up againt its competitors because it already sets an example in the SIEM world.
    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
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    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
    • Offense investigation was really helped in tackling the incidents. It was accurate and brief
    • The automation with IBM resilient (SOAR) was a milestone in elimination of user mistakes
    • The X-Force threat intelligence supported us in getting the work done without any 3rd party enterprise OSINT database
    Incentivized
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    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 Security QRadar SIEM Screenshots

    Screenshot of QRadar SIEM Cloud native- Threat intelligence preview