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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Cloudera Enterprise Data Hub

    Score9 out of 10
    N/AThe Cloudera Enterprise Data Hub powered by SDX is a multifunction analytics solution that supports a range of operational and analytic use cases for enterprises.N/A

    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)

    Pricing
    Cloudera Enterprise Data HubGoogle BigQuery
    Editions & Modules
    No answers on this topic
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    Offerings
    Pricing Offerings
    Cloudera Enterprise Data HubGoogle BigQuery
    Free Trial
    NoYes
    Free/Freemium Version
    YesYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Cloudera Enterprise Data HubGoogle BigQuery
    Considered Both Products
    Cloudera
    No answer on this topic
    Google
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    98%
    Would buy again
    64 Answers
    Delivers good value for the price
    No answers on this topic
    97%
    Delivers good value for the price
    56 Answers
    Happy with the feature set
    No answers on this topic
    97%
    Happy with the feature set
    63 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    100%
    Lived up to sales and marketing promises
    42 Answers
    Implementation went as expected
    No answers on this topic
    100%
    Implementation went as expected
    59 Answers
    Features
    Cloudera Enterprise Data HubGoogle BigQuery
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Cloudera Enterprise Data Hub and Google BigQuery
    Feature
    Cloudera Enterprise Data Hub
    -
    Ratings
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Automatic software patching00 Ratings8.017 Ratings
    Database scalability00 Ratings9.079 Ratings
    Automated backups00 Ratings8.524 Ratings
    Database security provisions00 Ratings8.873 Ratings
    Monitoring and metrics00 Ratings8.675 Ratings
    Automatic host deployment00 Ratings8.013 Ratings
    Best Alternatives
    Cloudera Enterprise Data HubGoogle BigQuery
    Small Businesses
    No answers on this topic
    MongoDB Atlas
    Score7.8 out of 10
    Medium-sized Companies
    Apache Hive
    Score8 out of 10
    Azure Database
    Score8.8 out of 10
    Enterprises
    Oracle Exadata
    Score9.8 out of 10
    Google Cloud SQL
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Cloudera Enterprise Data HubGoogle BigQuery
    Likelihood to Recommend
    9.0
    (12 ratings)
    9.0
    (79 ratings)
    Likelihood to Renew
    8.2
    (7 ratings)
    8.1
    (5 ratings)
    Usability
    -
    (0 ratings)
    6.6
    (6 ratings)
    Availability
    -
    (0 ratings)
    7.3
    (1 ratings)
    Performance
    -
    (0 ratings)
    6.4
    (1 ratings)
    Support Rating
    -
    (0 ratings)
    4.8
    (11 ratings)
    Configurability
    -
    (0 ratings)
    6.4
    (1 ratings)
    Contract Terms and Pricing Model
    -
    (0 ratings)
    10.0
    (1 ratings)
    Ease of integration
    -
    (0 ratings)
    7.3
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    7.3
    (1 ratings)
    Professional Services
    -
    (0 ratings)
    8.2
    (2 ratings)
    User Testimonials
    Cloudera Enterprise Data HubGoogle BigQuery
    Likelihood to Recommend
    Cloudera
    Cloudera excels at seamless migrations and upgrades.



    Cloudera supports self-healing and data center
    replacement of failed cloud instances while maintaining the state.



    Cloudera is essential to increase or decrease
    capacity through the user interface or API.



    Cloudera is great at simplifying big data analytics
    by providing the technology and tools needed to gain insights from IoT and
    connected devices to help monitor and condition our assets.



    Cloudera's cybersecurity platform option offers
    stronger anomaly detection, visibility, and prevention, as well as faster
    behavioral analysis.



    Cloudera is beneficial for enabling and utilizing
    the platform's machine learning and ad-hoc queries while securely storing,
    retrieving, and analyzing any volume of data at scale.
    Incentivized
    Read full review
    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
    Pros
    Cloudera
    • Excellent management capabilities via Cloudera Manager.
    • Open source and does not restrict our data to be bound by a proprietary format.
    • Offers excellent support for data governance and auditing.
    • Has all the components that would help us build a data hub.
    • Excellent platform support offered by Cloudera.
    Read full review
    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
    Cons
    Cloudera
    • Not fully Open Source, couple of components of the distributions are privately owned, meaning with public contributions are not welcome
    • Improvements to Cloudera manager can only be recommended. its very hard to get it done once recommended as the full control is with them.
    • Should make components more aligned to Open Source rather than making it closed sourced.
    • Custom Features of open source software tools supported only by Cloudera are tricky. Cant commit changes to tools like Hue.
    • Improvements to Cluster Management tool is required, which are already available to its competitors.
    Incentivized
    Read full review
    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
    Likelihood to Renew
    Cloudera
    Likely to renew the use in case the requirements for Cloudera remain valid. The rapid change in customer requirements and solutions that must be validated, integrated or tested changes. As the maturity of the solution increases, the requirements to renew use decrease. From a solution feature perspective by itself would probably grade 10.
    Read full review
    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
    Usability
    Cloudera
    No answers on this topic
    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
    Reliability and Availability
    Cloudera
    No answers on this topic
    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
    Performance
    Cloudera
    No answers on this topic
    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
    Support Rating
    Cloudera
    No answers on this topic
    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
    Alternatives Considered
    Cloudera
    Cloudera is
    compatible with Windows operating systems, and Mac allows cloud-based
    deployment, it is also very useful to configure data encryption, guarantee
    protocols, and security policies. It also provides integrated auditing and
    monitoring capabilities, as well as a control comprehensive data repository for
    the enterprise, and ensures vendor compatibility through its open-source
    architecture.
    Incentivized
    Read full review
    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
    Contract Terms and Pricing Model
    Cloudera
    No answers on this topic
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Scalability
    Cloudera
    No answers on this topic
    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
    Professional Services
    Cloudera
    No answers on this topic
    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
    Return on Investment
    Cloudera
    • Cloudera products are the most widely. It is more business friendly as data is more secure. The sensitive data that you operate on is local to you and your project rather than processing this data on Cloud.
    • Cloudera is definitely faster as wait time is reduced if on Cloud.
    • A lot range of products are covered. So it is definitely good for businesses and had good returns on investments.
    Read full review
    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
    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.