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Cloudera Data Science Workbench (discontinued) vs. Google BigQuery

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Cloudera Data Science Workbench (discontinued)

    Score6.7 out of 10
    N/ACloudera Data Science Workbench (CDSW) was an enterprise data science platform for collaborative development, experimentation, model training, deployment, and management on Cloudera data infrastructure. Cloudera Data Science Workbench has reached end of support. Cloudera states that its CDSW documentation is no longer updated.N/A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 Data Science Workbench (discontinued)Google 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 Data Science Workbench (discontinued)Google BigQuery
    Free Trial
    NoYes
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    Cloudera Data Science Workbench (discontinued)Google BigQuery
    Considered Both Products
    Discontinued Products
    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 Data Science Workbench (discontinued)Google BigQuery
    Platform Connectivity
    Comparison of Platform Connectivity features of Cloudera Data Science Workbench (discontinued) and Google BigQuery
    Feature
    Cloudera Data Science Workbench (discontinued)
    7.5
    2 Ratings
    11% below category average
    Google BigQuery
    -
    Ratings
    Connect to Multiple Data Sources7.02 Ratings00 Ratings
    Extend Existing Data Sources8.02 Ratings00 Ratings
    Automatic Data Format Detection7.02 Ratings00 Ratings
    MDM Integration8.02 Ratings00 Ratings
    Data Exploration
    Comparison of Data Exploration features of Cloudera Data Science Workbench (discontinued) and Google BigQuery
    Feature
    Cloudera Data Science Workbench (discontinued)
    7.6
    2 Ratings
    10% below category average
    Google BigQuery
    -
    Ratings
    Visualization7.12 Ratings00 Ratings
    Interactive Data Analysis8.02 Ratings00 Ratings
    Data Preparation
    Comparison of Data Preparation features of Cloudera Data Science Workbench (discontinued) and Google BigQuery
    Feature
    Cloudera Data Science Workbench (discontinued)
    7.8
    2 Ratings
    5% below category average
    Google BigQuery
    -
    Ratings
    Interactive Data Cleaning and Enrichment7.02 Ratings00 Ratings
    Data Transformations8.02 Ratings00 Ratings
    Data Encryption8.02 Ratings00 Ratings
    Built-in Processors8.02 Ratings00 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of Cloudera Data Science Workbench (discontinued) and Google BigQuery
    Feature
    Cloudera Data Science Workbench (discontinued)
    7.6
    2 Ratings
    11% below category average
    Google BigQuery
    -
    Ratings
    Multiple Model Development Languages and Tools8.02 Ratings00 Ratings
    Automated Machine Learning7.01 Ratings00 Ratings
    Single platform for multiple model development7.12 Ratings00 Ratings
    Self-Service Model Delivery8.12 Ratings00 Ratings
    Model Deployment
    Comparison of Model Deployment features of Cloudera Data Science Workbench (discontinued) and Google BigQuery
    Feature
    Cloudera Data Science Workbench (discontinued)
    8.0
    2 Ratings
    6% below category average
    Google BigQuery
    -
    Ratings
    Flexible Model Publishing Options8.12 Ratings00 Ratings
    Security, Governance, and Cost Controls7.82 Ratings00 Ratings
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Cloudera Data Science Workbench (discontinued) and Google BigQuery
    Feature
    Cloudera Data Science Workbench (discontinued)
    -
    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 Data Science Workbench (discontinued)Google BigQuery
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    MongoDB Atlas
    Score7.8 out of 10
    Medium-sized Companies
    Anaconda
    Score8.8 out of 10
    Azure Database
    Score8.8 out of 10
    Enterprises
    IBM Watson Studio
    Score10 out of 10
    Google Cloud SQL
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Cloudera Data Science Workbench (discontinued)Google BigQuery
    Likelihood to Recommend
    9.0
    (3 ratings)
    9.0
    (79 ratings)
    Likelihood to Renew
    -
    (0 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
    7.9
    (2 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 Data Science Workbench (discontinued)Google BigQuery
    Likelihood to Recommend
    Discontinued Products
    Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.
    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
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    Pros
    Discontinued Products
    • One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
    • For larger organizations/teams, it lets you be self reliant
    • As it sits on your cluster, it has very easy access of all the data on the HDFS
    • Linking with Github is a very good way to keep the code versions intact
    Incentivized
    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
    Discontinued Products
    • Installation is difficult.
    • Upgrades are difficult.
    • Licensing options are not flexible.
    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
    Discontinued Products
    No answers on this topic
    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
    Discontinued Products
    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
    Discontinued Products
    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
    Discontinued Products
    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
    Discontinued Products
    Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.
    Incentivized
    Read full review
    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
    Discontinued Products
    Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
    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
    Discontinued Products
    No answers on this topic
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Scalability
    Discontinued Products
    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
    Discontinued Products
    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
    Discontinued Products
    • Paid off for demonstration purposes.
    Incentivized
    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.