Amazon SageMaker vs. Cloudera Data Science Workbench vs. Posit

It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.

Incentivized

Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.

Incentivized

In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.

Incentivized

• Machine Learning at scale by deploying huge amount of training data
• Accelerated data processing for faster outputs and learnings
• Kubernetes integration for containerized deployments
• Creating API endpoints for use by technical users

Incentivized

• One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
• For larger organizations/teams, it lets you be self reliant
• As it sits on your cluster, it has very easy access of all the data on the HDFS
• Linking with Github is a very good way to keep the code versions intact

Incentivized

• The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
• The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
• Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.

Incentivized

• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Incentivized

• Installation is difficult.
• Upgrades are difficult.
• Licensing options are not flexible.

Incentivized

• Python integration is newer and still can be rough, especially with when using virtual environments.
• RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
• Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.

Incentivized

There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.

Incentivized

For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when

Incentivized

RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses

Incentivized

Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.

Incentivized

Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.

Incentivized

We did it at the individual level: anyone willing to code in R can use it. No real deployment involved.

Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

Incentivized

Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.

Incentivized

RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.

Incentivized

RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.

Incentivized

• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

Incentivized

• Paid off for demonstration purposes.

Incentivized

• Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
• Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
• What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).

Incentivized