KNIME Analytics Platform vs. Microsoft R Open / Revolution R Enterprise

KNIME Analytics Platform has vastly improved our effectiveness when working with large data sets. The self documenting GUI allows analysts to focus on what they are trying to accomplish, not complex code syntax. If we were to use traditional tools, like SQL, work would take much longer and it would be more difficult to collaborate both internally and with clients. Since KNIME Analytics Platform is database oriented, some spreadsheet functions are not supported, which is as it should be. For small data sets we often use Excel vlookup and pivot tables in place of KNIME Analytics Platform. If VBA code is requried, we go to KNIME Analytics Platform as we find VBA to be unstable in Excel.

Revolution Analytics is a very compelling product for Big Data Analytics. It allows distributed computing over multiple hadoop nodes thus allowing HDFS to do its role cleanly i.e. cheap massive storage and it does good job of running algorithms using R or similar programming language on Hadoop. It would be definitely advantage for the organization who uses either R or SAS as their statistical model development tool as Rev-R support both the platforms. Overall, very positive experience with Rev-R.

• Visual programming as oppose to scripting encourages data analysts to reap deeper insights from their data
• Large community contribution in extending the KNIME Analytics Platform into other areas of analytics, e.g. Text Analytics, Predictive Analytics, ML, etc.
• Open source with periodic updates ensures it is equipped to deal with the most sophisticated data analytics use case

• Parallel processing
• Integration with R
• Open-source

Incentivized

• Automation - e.g. RapidMiner Studio provides a Turbo Prep function, where one can get to working on models more quickly (RapidMiner is not open source though)
• KNIME does not replace a regular reporting tool - it is not meant to. However, if I have already spent some time developing a data acquisition and analytical model, it would be nice to be able to deploy, for example, a monitoring or reporting module that would process data autonomously and react accordingly.

Incentivized

• Very high learning curve

Incentivized

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

In general, Revolution Analytics brings a lot of value to the organization. The renewal decision would be based on return on investment in terms of quantified actionable insights that are getting generated against the cost of the product. Additionally, market brand of the tool and reputation risk in terms of possible acquisition and its impact to overall organizational analytic strategy would be considered as well.

The training KNIME Analytics Platform provide helps you get to grips with a product that is already very intuitive. There is a KNIME Analytics Platform way of thinking about addressing problems, but once you understand a couple of patterns which you see again and again in your workflow it all makes sense.

Incentivized

It is good, easy to use, improvements are being made to the product and more info being shared in the community. It just needs some more time to become more integrated to other platforms and tools/data out there.

Incentivized

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.

Incentivized

Generally support comes through the forums and user generated channels which are helpful, easy to access, quickly turned around and provided by knowledgeable users. However the support channels are not employees and the channels are often used as a way to learn quick difficult elements of R. Better design, users interface and tutorial options would alleviate the need for this sort of interaction.

Incentivized

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.

Incentivized

There are two aspects which put KNIME Analytics Platform ahead of other products. Firstly the fact that KNIME Analytics Platform comes at no cost and no restrictions on its use is an instant winner for any organisation wanting to democratise their data. It means that a client is free to install it on as many machines as they wish without worrying about costs, the number of seats required or payment models or procurement negotiation. It also means that we are not building costs into our clients business. Secondly, KNIME Analytics Platform has a very comprehensive set of tools for importing/exporting data, data manipulation and data science. Some products offer analytics packages on top of their base offering at additional cost and they are still not as comprehensive as what you get with KNIME Analytics Platform for free. For some types of analysis you may require to download additional packages with KNIME Analytics Platform, but its invariably at no cost, those packages are kept out of the main download to keep the size down. Due to the easy integration with R and Python, I view KNIME Analytics Platform as also having the capabilities of those languages too. This has helped me in the past with seamlessly importing a rare filetype and using very specific models not directly available in KNIME Analytics Platform.

Incentivized

R is decent for our needs but in the end didn't quite solve all of our needs so moved on. It is a good tool so far. its been a couple months since we last touched it so with changes continuing and more wide spread use and more info being published this tool will improve. Depending upon your needs this can be very easy for you to setup, use, and maintain when compared to other tools out there. My suggestion is to ensure you fully understand your use cases first with data sources identified to ensure this tool can meet your needs.

Incentivized

• 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.

Incentivized

• Better forecasting for resource allocation has saved our organisation hundreds of thousands in conjunction with other strategies.
• Better visualisation options has allowed smoother internal marketing and internal comms strategies when preparing teams for seasonality.

Incentivized