KNIME Analytics Platform vs. Microsoft Azure

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.

Actually, migrating to Microsoft Azure is a good solution for almost any situation, especially when all components of your network are ready to become cloud-based. The only drawback I personally encounter frequently is that older software packages cannot always be easily picked up and moved to Microsoft Azure in an optimal manner.

Incentivized

• 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

• Azure simply provides end to end life cycle. Starting from the development to automated deployment, you will find [a] bunch of options. Custom hook-points allow [integration] on-premise resources as well.
• Excellent documentation around all the services make it really easy for any novice. Overall support by [the] community and Azure Technical team is exceptional.
• BOT Services, Computer Vision services, ML frameworks provide excellent results as compare to similar services provided by other giants in the same space.
• Azure data services provide excellent support to ingest data from different sources, ETL, and consumption of data for BI purpose.

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

• In our experience, Azure Kubernetes Survice was difficult to set up, which is why we used Kubernetes on top of VMs.
• Azure REST API is a bit difficult to use, which made it difficult for us to automate our interactions with Azure.
• Azure's Web UI does a good job of showing metrics on individual VMs, but it would be great if there was a way to show certain metrics from multiple VMs on one dashboard. For example, hard drive usage on our database VMs.

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

We have been very satisfied with Windows Azure and now a lot of our business depends on it as more teams are now deploying their applications into Azure. Our next step is to have our Infrastructure team move their resources to Azure. It will take awhile for that to happen but we are positive that it will.

Incentivized

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

Microsoft Azure's overall usability has been better than expected. Often times vendors promise the world, only to leave you with a run-down town. Not the case with our experience. From an implementation perspective, all went perfect, and from the user-facing experience we have had no technical issues, just some learning curve issues that are more about "why" than "how"

Incentivized

It has proven to be unreliable in our production environment and services become unavailable without proper notification to system administrators

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

Support is easy with all the knowledge base articles available for free on the web. Plus, if you have a preferred status you can leverage their concierge support to get rapid response. Sometimes they’ll bounce you around a lot to get you to the right person, but they are quite responsive (especially when you are paying for the service). Many of the older Microsoft skills are also transferable from old-school on-prem to Azure-based virtual interfaces.

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

As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.

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

I feel that Microsoft Azure typically outperforms Google Cloud Platform in hybrid cloud capabilities, integration aspects, and, primarily, security compliance features. Azure offered superior integration with Microsoft's enterprise software ecosystem, and it's second to none in my opinion. This made it the natural choice for most, especially if heavily invested in Windows, Office 365, or Active Directory deployments. We chose Azure over GCP because we simply needed Windows workload support as a strong driver, more access to global regions, and let's not forget that most tech teams in an organization are Microsoft Certified, which makes skillset transfer from on-prem to cloud a minimal learning curve over shifting to a different provider.

• 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

• Times and growth went into it. By balancing on-premises maintenance with continuous cloud improvements, we’ve budgeted and planned endlessly increased capacity.
• In today’s world of cyber-crime, clients can put even more faith in what they’ve heard. We built an innovative single-sign-on hub for all users. Also, other business platforms use Azure application gateways, reducing worker switching time and increasing productivity.
• Its step can automate to improve the investment. In addition, we can integrate our organization’s credentials into an authorization for other systems.