Google App Engine is Google Cloud's platform-as-a-service offering. It features pay-per-use pricing and support for a broad array of programming languages.
$0.05
Per Hour Per Instance
KNIME Analytics Platform
Score 7.8 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
Microsoft Azure
Score 8.4 out of 10
N/A
Microsoft Azure is a cloud computing platform and infrastructure for building, deploying, and managing applications and services through a global network of Microsoft-managed datacenters.
$29
per month
Pricing
Google App Engine
KNIME Analytics Platform
Microsoft Azure
Editions & Modules
Starting Price
$0.05
Per Hour Per Instance
Max Price
$0.30
Per Hour Per Instance
KNIME Community Hub Personal Plan
$0
KNIME Analytics Platform
$0
KNIME Community Hub Team Plan
€99
per month 3 users
KNIME Business Hub
From €35,000
per year
Developer
$29
per month
Standard
$100
per month
Professional Direct
$1000
per month
Basic
Free
per month
Offerings
Pricing Offerings
Google App Engine
KNIME Analytics Platform
Microsoft Azure
Free Trial
No
No
Yes
Free/Freemium Version
Yes
Yes
Yes
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
—
—
The free tier lets users have access to a variety of services free for 12 months with limited usage after making an Azure account.
Compared with Microsoft Azure, Google App Engine requires a more complicated development environment setup. It's not as simple as using Visual Studio 2015 with Azure SDK. There are multiple IDE on the market to choose from for developing apps for Google App Engine. JetBrains …
If you have a small team which is also responsible for development of the product then surely go for it. And if you have a larger team with dedicated person to take care of deployments. Go for cheaper options such as compute engine or AWS (be sure to do your research on pricing …
We were on another much smaller cloud provider and decided to make the switch for several reasons - stability, breadth of services, and security. In reviewing options, GCP provided the best mixtures of meeting our needs while also balancing the overall cost of the service as …
I think that Microsoft and Amazon are simply investing more in their offerings, and there are a bunch of cool PaaS solutions out there as well. Google App Engine is solid, and is probably the right choice for some projects. But ultimately one should evaluate each platform …
We have settled with Microsoft Azure considered its effective administration and the ability to data visualization and analysis, together with the top-notch security/stability.
App Engine is such a good resource for our team both internally and externally. You have complete control over your app, how it runs, when it runs, and more while Google handles the back-end, scaling, orchestration, and so on. If you are serving a tool, system, or web page, it's perfect. If you are serving something back-end, like an automation or ETL workflow, you should be a little considerate or careful with how you are structuring that job. For instance, the Standard environment in Google App Engine will present you with a resource limit for your server calls. If your operations are known to take longer than, say, 10 minutes or so, you may be better off moving to the Flexible environment (which may be a little more expensive but certainly a little more powerful and a little less limited) or even moving that workflow to something like Google Compute Engine or another managed service.
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.
Azure is particularly well suited for enterprise environments with existing Microsoft investments, those that require robust compliance features, and organizations that need hybrid cloud capabilities that bridge on-premises and cloud infrastructure. In my opinion, Azure is less appropriate for cost-sensitive startups or small businesses without dedicated cloud expertise and scenarios requiring edge computing use cases with limited connectivity. Azure offers comprehensive solutions for most business needs but can feel like there is a higher learning curve than other cloud-based providers, depending on the product and use case.
Microsoft Azure is highly scalable and flexible. You can quickly scale up or down additional resources and computing power.
You have no longer upfront investments for hardware. You only pay for the use of your computing power, storage space, or services.
The uptime that can be achieved and guaranteed is very important for our company. This includes the rapid maintenance for security updates that are mostly carried out by Microsoft.
The wide range of capabilities of services that are possible in Microsoft Azure. You can practically put or create anything in Microsoft Azure.
There is a slight learning curve to getting used to code on Google App Engine.
Google Cloud Datastore is Google's NoSQL database in the cloud that your applications can use. NoSQL databases, by design, cannot give handle complex queries on the data. This means that sometimes you need to think carefully about your data structures - so that you can get the results you need in your code.
Setting up billing is a little annoying. It does not seem to save billing information to your account so you can re-use the same information across different Cloud projects. Each project requires you to re-enter all your billing information (if required)
The cost of resources is difficult to determine, technical documentation is frequently out of date, and documentation and mapping capabilities are lacking.
The documentation needs to be improved, and some advanced configuration options require research and experimentation.
Microsoft's licensing scheme is too complex for the average user, and Azure SQL syntax is too different from traditional SQL.
App Engine is a solid choice for deployments to Google Cloud Platform that do not want to move entirely to a Kubernetes-based container architecture using a different Google product. For rapid prototyping of new applications and fairly straightforward web application deployments, we'll continue to leverage the capabilities that App Engine affords us.
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
Moving to Azure was and still is an organizational strategy and not simply changing vendors. Our product roadmap revolved around Azure as we are in the business of humanitarian relief and Azure and Microsoft play an important part in quickly and efficiently serving all of the world. Migration and investment in Azure should be considered as an overall strategy of an organization and communicated companywide.
I had to revisit the UI after a year of just setting up and forgetting. The UI got some improvements but the amount of navigation we have to go through to setup a new app has increased but also got easier to setup. Gemini now is integrated and make getting answers faster
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
As Microsoft Azure is [doing a] really good with PaaS. The need of a market is to have [a] combo of PaaS and IaaS. While AWS is making [an] exceptionally well blend of both of them, Azure needs to work more on DevOps and Automation stuff. Apart from that, I would recommend Azure as a great platform for cloud services as scale.
Good amount of documentation available for Google App Engine and in general there is large developer community around Google App Engine and other products it interacts with. Lastly, Google support is great in general. No issues so far with them.
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.
We were running Windows Server and Active Directory, so [Microsoft] Azure was a seamless transition. We ran into a few, if any support issues, however, the availability of Microsoft Azure's support team was more than willing and able to guide us through the process. They even proposed solutions to issues we had not even thought of!
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.
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.
We were on another much smaller cloud provider and decided to make the switch for several reasons - stability, breadth of services, and security. In reviewing options, GCP provided the best mixtures of meeting our needs while also balancing the overall cost of the service as compared to the other major players in Azure and AWS.
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].
As I continue to evaluate the "big three" cloud providers for our clients, I make the following distinctions, though this gap continues to close. AWS is more granular, and inherently powerful in the configuration options compared to [Microsoft] Azure. It is a "developer" platform for cloud. However, Azure PowerShell is helping close this gap. Google Cloud is the leading containerization platform, largely thanks to it building kubernetes from the ground up. Azure containerization is getting better at having the same storage/deployment options.
Effective integration to other java based frameworks.
Time to market is very quick. Build, test, deploy and use.
The GAE Whitelist for java is an important resource to know what works and what does not. So use it. It would also be nice for Google to expand on items that are allowed on GAE platform.
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.
For about 2 years we didn't have to do anything with our production VMs, the system ran without a hitch, which meant our engineers could focus on features rather than infrastructure.
DNS management was very easy in Azure, which made it easy to upgrade our cluster with zero downtime.
Azure Web UI was easy to work with and navigate, which meant our senior engineers and DevOps team could work with Azure without formal training.
