Compare H2O.ai vs. KNIME

H2O.ai is ranked 6th in Data Science Platforms with 6 reviews while KNIME is ranked 2nd in Data Science Platforms with 10 reviews. H2O.ai is rated 7.6, while KNIME is rated 8.4. The top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". 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". H2O.ai is most compared with KNIME, Microsoft Azure Machine Learning Studio and Dataiku Data Science Studio, whereas KNIME is most compared with Alteryx, RapidMiner and Weka. See our H2O.ai vs. KNIME report.
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H2O.ai Logo
5,524 views|3,832 comparisons
KNIME Logo
Read 10 KNIME reviews.
21,168 views|16,525 comparisons
Most Helpful Review
Find out what your peers are saying about H2O.ai vs. KNIME and other solutions. Updated: November 2019.
378,124 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
One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm.The ease of use in connecting to our cluster machines.It is helpful, intuitive, and easy to use. The learning curve is not too steep.AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms.Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O.

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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 interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability.I would like to see more features related to deployment.The model management features could be improved.It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O.

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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.​The 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
We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff.

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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
6th
Views
5,524
Comparisons
3,832
Reviews
6
Average Words per Review
294
Avg. Rating
7.7
2nd
Views
21,168
Comparisons
16,525
Reviews
10
Average Words per Review
333
Avg. Rating
8.4
Top Comparisons
Compared 25% of the time.
Compared 41% of the time.
Compared 14% of the time.
Compared 7% of the time.
Also Known As
KNIME Analytics Platform
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H2O.ai
Knime
Overview

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

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
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Learn more about KNIME
Sample Customers
poder.io, Stanley Black & Decker, G5, PWC, Comcast, CiscoInfocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Top Industries
VISITORS READING REVIEWS
Software R&D Company45%
Comms Service Provider13%
Financial Services Firm8%
Transportation Company7%
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 H2O.ai vs. KNIME and other solutions. Updated: November 2019.
378,124 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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