What users are saying about
13 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener noreferrer'>trScore algorithm: Learn more.</a>
Score 7.9 out of 100
32 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener noreferrer'>trScore algorithm: Learn more.</a>
Score 7.8 out of 100

Likelihood to Recommend

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

KNIME Analytics Platform

If you have a team of engineers or data scientists who do not like to code, KNIME can be a good platform to build quick and dirty pipelines. However if you are moving away from R&D to deployment, KNIME lacks the scalability compared to Python or R itself. When deploying, you can choose to output json or use their native front end from KNIME Server, but KNIME Server is not free.
Ivan Cui | TrustRadius Reviewer

Feature Rating Comparison

Platform Connectivity

Data Science Workbench
7.3
KNIME Analytics Platform
7.4
Connect to Multiple Data Sources
Data Science Workbench
6.6
KNIME Analytics Platform
8.5
Extend Existing Data Sources
Data Science Workbench
7.6
KNIME Analytics Platform
7.2
Automatic Data Format Detection
Data Science Workbench
7.0
KNIME Analytics Platform
7.8
MDM Integration
Data Science Workbench
8.0
KNIME Analytics Platform
6.0

Data Exploration

Data Science Workbench
8.2
KNIME Analytics Platform
5.2
Visualization
Data Science Workbench
7.9
KNIME Analytics Platform
5.0
Interactive Data Analysis
Data Science Workbench
8.4
KNIME Analytics Platform
5.5

Data Preparation

Data Science Workbench
7.8
KNIME Analytics Platform
6.1
Interactive Data Cleaning and Enrichment
Data Science Workbench
7.4
KNIME Analytics Platform
7.0
Data Transformations
Data Science Workbench
8.0
KNIME Analytics Platform
7.0
Data Encryption
Data Science Workbench
8.0
KNIME Analytics Platform
4.6
Built-in Processors
Data Science Workbench
7.6
KNIME Analytics Platform
5.9

Platform Data Modeling

Data Science Workbench
8.2
KNIME Analytics Platform
5.6
Multiple Model Development Languages and Tools
Data Science Workbench
8.4
KNIME Analytics Platform
6.4
Automated Machine Learning
Data Science Workbench
7.0
KNIME Analytics Platform
4.4
Single platform for multiple model development
Data Science Workbench
8.3
KNIME Analytics Platform
5.7
Self-Service Model Delivery
Data Science Workbench
8.9
KNIME Analytics Platform
5.9

Model Deployment

Data Science Workbench
7.6
KNIME Analytics Platform
5.1
Flexible Model Publishing Options
Data Science Workbench
8.9
KNIME Analytics Platform
5.6
Security, Governance, and Cost Controls
Data Science Workbench
6.3
KNIME Analytics Platform
4.7

Pros

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

KNIME Analytics Platform

  • KNIME works better than most tools for ETL functions.
  • Easy to track the different steps
  • Easy to isolate and fix specific workflow steps.
Anonymous | TrustRadius Reviewer

Cons

Data Science Workbench

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

KNIME Analytics Platform

  • Visualization can be improved further though it has been better with new versions, with a lot of scope available. However, connectivity to Tableau somehow overcomes this. Also, skilled resources are difficult to find for KNIME, due to other solutions having better penetration.
  • Knowledge of R/Python is required to fully use the statistical analysis (rather than just data mining). Also, memory usage is a problematic issue sometimes.
  • Not enough domain usage experience can be shared between KNIME users as well.
Rohit Narang | TrustRadius Reviewer

Likelihood to Renew

Data Science Workbench

No score
No answers yet
No answers on this topic

KNIME Analytics Platform

KNIME Analytics Platform 8.0
Based on 1 answer
I am happy with the product. It provides the required functionality.
Anonymous | TrustRadius Reviewer

Usability

Data Science Workbench

No score
No answers yet
No answers on this topic

KNIME Analytics Platform

KNIME Analytics Platform 8.0
Based on 1 answer
It performs all the required functions.
Anonymous | TrustRadius Reviewer

Support Rating

Data Science Workbench

Data Science Workbench 6.7
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

KNIME Analytics Platform

KNIME Analytics Platform 6.7
Based on 4 answers
Since it is relatively new, there has not developed a vast previously asked/frequently asked questions library that comes up when you google an issue you come across with. This will happen only in time, and as the community grows. Because of the same reason, the community is not big. Consequently, it is possible not to receive good, fast responses to asked questions in community hubs and forums.
Anonymous | TrustRadius Reviewer

Alternatives Considered

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

KNIME Analytics Platform

KNIME is a lower price point and has strong cross platform capabilities. Other platforms are locked to a specific operating system and cost in some cases substantially more, making them less good choices for smaller businesses that still need basic data unification. The fact that KNIME is OS-independent is a big positive.
Christopher Penn | TrustRadius Reviewer

Return on Investment

Data Science Workbench

  • Paid off for demonstration purposes.
Anonymous | TrustRadius Reviewer

KNIME Analytics Platform

  • Lowest TCO compared to other tools
  • Accelerates analysis - the analysts can dedicate more time to analysis itself, not to data preparation
Viktor Mulac | TrustRadius Reviewer

Pricing Details

Data Science Workbench

General

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

KNIME Analytics Platform

General

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

Rating Summary

Likelihood to Recommend

Data Science Workbench
7.7
KNIME Analytics Platform
7.7

Likelihood to Renew

Data Science Workbench
KNIME Analytics Platform
8.0

Usability

Data Science Workbench
KNIME Analytics Platform
8.0

Support Rating

Data Science Workbench
6.7
KNIME Analytics Platform
6.7

Implementation Rating

Data Science Workbench
KNIME Analytics Platform
8.0

Add comparison