What users are saying about
145 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 8.8 out of 100
Based on 145 reviews and ratings
14 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 7.3 out of 100
Based on 14 reviews and ratings
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
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
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
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
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
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.
Owner, previous CEO
Econometric StudiosFinancial Services, 11-50 employees
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.

Verified User
Professional in Information Technology
Telecommunications Company, 1001-5000 employeesPros
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
Software Engineer
LinkedInInternet, 5001-10,000 employees
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
Sr.Technical Manager/Delivery Manager
Nisum Technologies, Inc.Retail, 10,001+ employees
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
Data Czar
Envisagenics, Inc.Marketing and Advertising, 51-200 employees
Data Science Workbench
- Installation is difficult.
- Upgrades are difficult.
- Licensing options are not flexible.

Verified User
Professional in Research & Development
Utilities Company, 10,001+ employeesPricing 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.
Senior Software Developer (Consultant)
Morgan StanleyBanking, 10,001+ employees
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.

Verified User
Engineer in Information Technology
Information Technology & Services Company, 11-50 employeesData 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.
Technical Manager
Rishabh Software Private LimitedInformation Technology & Services, 501-1000 employees
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.

Verified User
Professional in Information Technology
Telecommunications Company, 1001-5000 employeesAlternatives 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.

Verified User
Engineer in Engineering
Computer Software Company, 51-200 employeesData 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.
Sr.Technical Manager/Delivery Manager
Nisum Technologies, Inc.Retail, 10,001+ employees
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
Senior Data Engineer
A.P. Moller - MaerskLogistics & Supply Chain, 10,001+ employees
Data Science Workbench
- Paid off for demonstration purposes.

Verified User
Professional in Research & Development
Utilities Company, 10,001+ employees