What users are saying about
145 Ratings
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Score 8.8 out of 100
14 Ratings
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Score 7.3 out of 100

Feature Set Ratings

    Platform Connectivity

    Apache Spark

    Feature Set Not Supported
    N/A
    7.5

    Data Science Workbench

    75%
    Cloudera Data Science Workbench ranks higher in 4/4 features

    Connect to Multiple Data Sources

    N/A
    0 Ratings
    7.0
    70%
    2 Ratings

    Extend Existing Data Sources

    N/A
    0 Ratings
    8.0
    80%
    2 Ratings

    Automatic Data Format Detection

    N/A
    0 Ratings
    7.0
    70%
    2 Ratings

    MDM Integration

    N/A
    0 Ratings
    8.0
    80%
    2 Ratings

    Data Exploration

    Apache Spark

    Feature Set Not Supported
    N/A
    7.6

    Data Science Workbench

    76%
    Cloudera Data Science Workbench ranks higher in 2/2 features

    Visualization

    N/A
    0 Ratings
    7.1
    71%
    2 Ratings

    Interactive Data Analysis

    N/A
    0 Ratings
    8.0
    80%
    2 Ratings

    Data Preparation

    Apache Spark

    Feature Set Not Supported
    N/A
    7.8

    Data Science Workbench

    78%
    Cloudera Data Science Workbench ranks higher in 4/4 features

    Interactive Data Cleaning and Enrichment

    N/A
    0 Ratings
    7.0
    70%
    2 Ratings

    Data Transformations

    N/A
    0 Ratings
    8.0
    80%
    2 Ratings

    Data Encryption

    N/A
    0 Ratings
    8.0
    80%
    2 Ratings

    Built-in Processors

    N/A
    0 Ratings
    8.0
    80%
    2 Ratings

    Platform Data Modeling

    Apache Spark

    Feature Set Not Supported
    N/A
    7.6

    Data Science Workbench

    76%
    Cloudera Data Science Workbench ranks higher in 4/4 features

    Multiple Model Development Languages and Tools

    N/A
    0 Ratings
    8.0
    80%
    2 Ratings

    Automated Machine Learning

    N/A
    0 Ratings
    7.0
    70%
    1 Rating

    Single platform for multiple model development

    N/A
    0 Ratings
    7.1
    71%
    2 Ratings

    Self-Service Model Delivery

    N/A
    0 Ratings
    8.1
    81%
    2 Ratings

    Model Deployment

    Apache Spark

    Feature Set Not Supported
    N/A
    8.0

    Data Science Workbench

    80%
    Cloudera Data Science Workbench ranks higher in 2/2 features

    Flexible Model Publishing Options

    N/A
    0 Ratings
    8.1
    81%
    2 Ratings

    Security, Governance, and Cost Controls

    N/A
    0 Ratings
    7.8
    78%
    2 Ratings

    Attribute Ratings

    • Apache Spark is rated higher in 2 areas: Likelihood to Recommend, Support Rating

    Likelihood to Recommend

    9.2

    Apache Spark

    92%
    22 Ratings
    8.9

    Data Science Workbench

    89%
    3 Ratings

    Likelihood to Renew

    10.0

    Apache Spark

    100%
    1 Rating

    Data Science Workbench

    N/A
    0 Ratings

    Usability

    9.4

    Apache Spark

    94%
    2 Ratings

    Data Science Workbench

    N/A
    0 Ratings

    Support Rating

    8.7

    Apache Spark

    87%
    6 Ratings
    7.7

    Data Science Workbench

    77%
    3 Ratings

    Likelihood to Recommend

    Apache Spark

    The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
    Thomas Young | TrustRadius Reviewer

    Data Science Workbench

    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.
    Anonymous | TrustRadius Reviewer

    Pros

    Apache Spark

    • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
    • Faster in execution times compare to Hadoop and PIG Latin
    • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
    • Interoperability between SQL and Scala / Python style of munging data
    Nitin Pasumarthy | TrustRadius Reviewer

    Data Science Workbench

    • 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
    Bharadwaj (Brad) Chivukula | TrustRadius Reviewer

    Cons

    Apache Spark

    • Memory management. Very weak on that.
    • PySpark not as robust as scala with spark.
    • spark master HA is needed. Not as HA as it should be.
    • Locality should not be a necessity, but does help improvement. But would prefer no locality
    Anson Abraham | TrustRadius Reviewer

    Data Science Workbench

    • Installation is difficult.
    • Upgrades are difficult.
    • Licensing options are not flexible.
    Anonymous | TrustRadius Reviewer

    Pricing Details

    Apache Spark

    General

    Free Trial
    Free/Freemium Version
    Premium Consulting/Integration Services
    Entry-level set up fee?
    No

    Starting Price

    Data Science Workbench

    General

    Free Trial
    Free/Freemium Version
    Premium Consulting/Integration Services
    Entry-level set up fee?
    No

    Starting Price

    Likelihood to Renew

    Apache Spark

    Apache Spark 10.0
    Based on 1 answer
    Capacity of computing data in cluster and fast speed.
    Steven Li | TrustRadius Reviewer

    Data Science Workbench

    No score
    No answers yet
    No answers on this topic

    Usability

    Apache Spark

    Apache Spark 9.4
    Based on 2 answers
    The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
    Anonymous | TrustRadius Reviewer

    Data Science Workbench

    No score
    No answers yet
    No answers on this topic

    Support Rating

    Apache Spark

    Apache Spark 8.7
    Based on 6 answers
    1. It integrates very well with scala or python.2. It's very easy to understand SQL interoperability.3. Apache is way faster than the other competitive technologies.4. The support from the Apache community is very huge for Spark.5. Execution times are faster as compared to others.6. There are a large number of forums available for Apache Spark.7. The code availability for Apache Spark is simpler and easy to gain access to.8. Many organizations use Apache Spark, so many solutions are available for existing applications.
    Yogesh Mhasde | TrustRadius Reviewer

    Data Science Workbench

    Data Science Workbench 7.7
    Based on 3 answers
    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.
    Anonymous | TrustRadius Reviewer

    Alternatives Considered

    Apache Spark

    Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
    Anonymous | TrustRadius Reviewer

    Data Science Workbench

    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.
    Bharadwaj (Brad) Chivukula | TrustRadius Reviewer

    Return on Investment

    Apache Spark

    • Business leaders are able to take data driven decisions
    • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
    • Business is able come up with new product ideas
    Surendranatha Reddy Chappidi | TrustRadius Reviewer

    Data Science Workbench

    • Paid off for demonstration purposes.
    Anonymous | TrustRadius Reviewer

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