Compare Dataiku Data Science Studio vs. KNIME

Dataiku Data Science Studio is ranked 9th in Data Science Platforms with 2 reviews while KNIME is ranked 2nd in Data Science Platforms with 10 reviews. Dataiku Data Science Studio is rated 7.6, while KNIME is rated 8.4. The top reviewer of Dataiku Data Science Studio writes "GUI-based functionality is easy to use, but server up-time needs to be improved". On the other hand, the top reviewer of KNIME writes "Our average record size was around 10 million records. If we have bigger data, we can opt for a Big Data extension for Hadoop, Spark, etc". Dataiku Data Science Studio is most compared with Alteryx, KNIME and Databricks, whereas KNIME is most compared with Alteryx, RapidMiner and Weka.
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Read 10 KNIME reviews.
21,168 views|16,525 comparisons
Most Helpful Review
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377,556 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction.Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors.Cloud-based process run helps in not keeping the systems on while processes are running.

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This solution is easy to use and especially good at data preparation and wrapping.It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop.Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis.Clear view of the data at every step of ETL process enables changing the flow as needed.We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders.The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine.Easy to connect with every database: We use queries from SQL, Redshift, Oracle.We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics.

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Cons
The ability to have charts right from the explorer would be an improvement.Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders.

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It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end.They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning.The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R).The program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily.‚ÄčThe data visualization part is the area most in need of improvement.The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best.Data visualization needs improvement.I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something.

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Pricing and Cost Advice
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KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required.

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Ranking
9th
Views
7,243
Comparisons
5,225
Reviews
1
Average Words per Review
336
Avg. Rating
7.0
2nd
Views
21,168
Comparisons
16,525
Reviews
10
Average Words per Review
333
Avg. Rating
8.4
Top Comparisons
Compared 15% of the time.
Compared 41% of the time.
Compared 14% of the time.
Compared 7% of the time.
Also Known As
Dataiku DSSKNIME Analytics Platform
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Dataiku
Knime
Overview

Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.

KNIME is the leading open platform for data-driven innovation helping organizations to stay ahead of change. Use our open-source, enterprise-grade analytics platform to discover the potential hidden in your data, mine for fresh insights or predict new futures.
Offer
Learn more about Dataiku Data Science Studio
Learn more about KNIME
Sample Customers
BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAutoInfocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Top Industries
VISITORS READING REVIEWS
Software R&D Company32%
Financial Services Firm14%
Comms Service Provider8%
Government6%
VISITORS READING REVIEWS
Software R&D Company23%
Comms Service Provider16%
Manufacturing Company9%
Financial Services Firm9%
Company Size
No Data Available
REVIEWERS
Small Business27%
Midsize Enterprise27%
Large Enterprise45%
VISITORS READING REVIEWS
Small Business17%
Midsize Enterprise2%
Large Enterprise82%
Find out what your peers are saying about Alteryx, Knime, IBM and others in Data Science Platforms. Updated: October 2019.
377,556 professionals have used our research since 2012.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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