What users are saying about
127 Ratings
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Score 8.7 out of 100
14 Ratings
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Score 7.5 out of 100

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

Feature Rating Comparison

Platform Connectivity

Apache Spark
Data Science Workbench
7.3
Connect to Multiple Data Sources
Apache Spark
Data Science Workbench
6.7
Extend Existing Data Sources
Apache Spark
Data Science Workbench
7.7
Automatic Data Format Detection
Apache Spark
Data Science Workbench
7.0
MDM Integration
Apache Spark
Data Science Workbench
8.0

Data Exploration

Apache Spark
Data Science Workbench
8.0
Visualization
Apache Spark
Data Science Workbench
7.6
Interactive Data Analysis
Apache Spark
Data Science Workbench
8.3

Data Preparation

Apache Spark
Data Science Workbench
7.8
Interactive Data Cleaning and Enrichment
Apache Spark
Data Science Workbench
7.3
Data Transformations
Apache Spark
Data Science Workbench
8.0
Data Encryption
Apache Spark
Data Science Workbench
8.0
Built-in Processors
Apache Spark
Data Science Workbench
7.7

Platform Data Modeling

Apache Spark
Data Science Workbench
8.0
Multiple Model Development Languages and Tools
Apache Spark
Data Science Workbench
8.3
Automated Machine Learning
Apache Spark
Data Science Workbench
7.0
Single platform for multiple model development
Apache Spark
Data Science Workbench
7.9
Self-Service Model Delivery
Apache Spark
Data Science Workbench
8.6

Model Deployment

Apache Spark
Data Science Workbench
7.7
Flexible Model Publishing Options
Apache Spark
Data Science Workbench
8.6
Security, Governance, and Cost Controls
Apache Spark
Data Science Workbench
6.8

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

Usability

Apache Spark

Apache Spark 8.7
Based on 3 answers
Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.
Partha Protim Pegu | TrustRadius Reviewer

Data Science Workbench

No score
No answers yet
No answers on this topic

Support Rating

Apache Spark

Apache Spark 8.3
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 7.1
Based on 2 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

  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
Carla Borges | TrustRadius Reviewer

Data Science Workbench

  • Paid off for demonstration purposes.
Anonymous | TrustRadius Reviewer

Pricing Details

Apache Spark

General

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

Data Science Workbench

General

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

Rating Summary

Likelihood to Recommend

Apache Spark
8.6
Data Science Workbench
8.1

Usability

Apache Spark
8.7
Data Science Workbench

Support Rating

Apache Spark
8.3
Data Science Workbench
7.1

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