Cloudera Data Science Workbench vs. KNIME Analytics Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Data Science Workbench
Score 6.7 out of 10
N/A
Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.N/A
KNIME Analytics Platform
Score 8.3 out of 10
N/A
KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.
$0
per month
Pricing
Cloudera Data Science WorkbenchKNIME Analytics Platform
Editions & Modules
No answers on this topic
KNIME Community Hub - Individual
$0
KNIME Community Hub - Team
From €250
per month Starts from 3 users
Offerings
Pricing Offerings
Data Science WorkbenchKNIME Analytics Platform
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 WorkbenchKNIME Analytics Platform
Top Pros
Top Cons
Features
Cloudera Data Science WorkbenchKNIME Analytics Platform
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Cloudera Data Science Workbench
7.5
2 Ratings
12% below category average
KNIME Analytics Platform
9.1
19 Ratings
7% above category average
Connect to Multiple Data Sources7.02 Ratings9.619 Ratings
Extend Existing Data Sources8.02 Ratings10.010 Ratings
Automatic Data Format Detection7.02 Ratings9.019 Ratings
MDM Integration8.02 Ratings7.98 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Cloudera Data Science Workbench
7.6
2 Ratings
10% below category average
KNIME Analytics Platform
8.0
18 Ratings
5% below category average
Visualization7.12 Ratings8.018 Ratings
Interactive Data Analysis8.02 Ratings8.018 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Cloudera Data Science Workbench
7.8
2 Ratings
5% below category average
KNIME Analytics Platform
8.3
19 Ratings
1% above category average
Interactive Data Cleaning and Enrichment7.02 Ratings9.019 Ratings
Data Transformations8.02 Ratings9.419 Ratings
Data Encryption8.02 Ratings7.47 Ratings
Built-in Processors8.02 Ratings7.48 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Cloudera Data Science Workbench
7.6
2 Ratings
11% below category average
KNIME Analytics Platform
7.9
18 Ratings
7% below category average
Multiple Model Development Languages and Tools8.02 Ratings9.417 Ratings
Automated Machine Learning7.01 Ratings8.217 Ratings
Single platform for multiple model development7.12 Ratings9.218 Ratings
Self-Service Model Delivery8.12 Ratings5.08 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Cloudera Data Science Workbench
8.0
2 Ratings
7% below category average
KNIME Analytics Platform
7.3
11 Ratings
16% below category average
Flexible Model Publishing Options8.12 Ratings8.611 Ratings
Security, Governance, and Cost Controls7.82 Ratings5.94 Ratings
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Cloudera Data Science WorkbenchKNIME Analytics Platform
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User Ratings
Cloudera Data Science WorkbenchKNIME Analytics Platform
Likelihood to Recommend
9.0
(3 ratings)
9.6
(22 ratings)
Likelihood to Renew
-
(0 ratings)
9.4
(4 ratings)
Usability
-
(0 ratings)
9.0
(3 ratings)
Support Rating
7.9
(2 ratings)
9.0
(6 ratings)
Implementation Rating
-
(0 ratings)
7.0
(2 ratings)
Ease of integration
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Cloudera Data Science WorkbenchKNIME Analytics Platform
Likelihood to Recommend
Cloudera
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.
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KNIME
KNIME Analytics Platform is excellent for people who are finding Excel frustrating, this can be due to errors creeping in due to manual changes or simply that there are too many calculations which causes the system to slow down and crash. This is especially true for regular reporting where a KNIME Analytics Platform workflow can pull in the most recent data, process it and provide the necessary output in one click. I find KNIME Analytics Platform especially useful when talking with audiences who are intimidated by code. KNIME Analytics Platform allows us to discuss exactly how data is processed and an analysis takes place at an abstracted level where non-technical users are happy to think and communicate which is often essential when they are subject matter experts whom you need for guidance. For experienced programmers KNIME Analytics Platform is a double-edged sword. Often programmers wish to write their own code because they are more efficient working that way and are constrained by having to think and implement work in nodes. However, those constraints forcing development in a "KNIME way" are useful when working in teams and for maintenance compared to some programmers' idiosyncratic styles.
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Pros
Cloudera
  • 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
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KNIME
  • Summarize instrument level financial data with relevant statistics
  • Map transactions from core extracts to groups of like transactions using rule engines
  • Machine learning using random forests and other techniques to analyze data and identify correlations for use in predictive models
  • Fill out sampling data from averages.
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Cons
Cloudera
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
Read full review
KNIME
  • It does not have proper visualization.
  • Some other BI tools (QlikView) have much easier functions for data interaction.
  • Some other BI tools (Tableau) can be set up much faster.
  • It is not an easy tool to use for non-tech savvy staff.
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Likelihood to Renew
Cloudera
No answers on this topic
KNIME
We are happy with Knime product and their support. Knime AP is versatile product and even can execute Python scripts if needed. It also supports R execution as well; however, it is not being used at our end
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Usability
Cloudera
No answers on this topic
KNIME
KNIME Analytics Platform offers a great tradeoff between intuitiveness and simplicity of the user interface and almost limitless flexibility. There are tools that are even easier to adopt by someone new to analytics, but none that would provide the scalability of KNIME when the user skills and application complexity grows
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Support Rating
Cloudera
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.
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KNIME
KNIME's HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME's documentation is also helpful but it does not provide you all the answers you need some of the time.
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Implementation Rating
Cloudera
No answers on this topic
KNIME
KNIME Analytics Platform is easy to install on any Windows, Mac or Linux machine. The KNIME Server product that is currently being replaced by the KNIME Business Hub comes as multiple layers of software and it took us some time to set up the system right for stability. This was made harder by KNIME staff's deeper expertise in setting up the Server in Linux rather than Windows environment. The KNIME Business Hub promises to have a simpler architecture, although currently there is no visibility of a Windows version of the product.
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Alternatives Considered
Cloudera
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.
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KNIME
Having used both the Alteryx and [KNIME Analytics] I can definitely feel the ease of using the software of Alteryx. The [KNIME Analytics] on the other hand isn't that great but is 90% of what Alteryx can do along with how much ease it can do. Having said that, the 90% functionality and UI at no cost would be enough for me to quit using Alteryx and move towards [KNIME Analytics].
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Return on Investment
Cloudera
  • Paid off for demonstration purposes.
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KNIME
  • It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
  • Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.
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ScreenShots

KNIME Analytics Platform Screenshots

Screenshot of the KNIME Modern UI. This is the the new user interface for the KNIME Analytics Platform that is available with improved look and feel as the default interface, from KNIME Analytics Platform version 5.1.0 release.Screenshot of the KNIME Analytics Platform user interface - the KNIME Workbench - displays the current, open workflow(s). Here is the general user interface layout — application tabs, side panel, workflow editor and node monitor.Screenshot of the KNIME user interface elements — workflow toolbar, node action bar, rename components and metanodes.Screenshot of the entry page, which is displayed by clicking the Home tab. From here users can; check out example workflows to get started, access a local workspace, or even start a new workflow by clicking the yellow plus button.Screenshot of the status of a KNIME node, which shows whether it's configured, not configured, executed, or has an error.Screenshot of the KNIME node action bar, which can be used to configure, execute, cancel, reset, and - when available - open the view.