Cloudera Data Science Workbench vs. Dataiku

• If you already have a Cloudera partnership and a cluster, having this is a no brainer.
• It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
• With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.

Incentivized

I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.

Incentivized

• Enterprise grade security.
• Self-service analytics platform.
• Popular programming support.

Incentivized

• Very intuitive and easy to use UI, making a lot of types of users can collaborate with each other easily, by visualizing the same workflow.
• Many building blocks can be reused immediately, avoid a lot of non-standard boiler plate implementation.
• Data pre-analysis and feature engineering assistance increase the productivity as well as the efficiency of data scientists.
• Many data connectors support wide range of data storage, from SQL, TeraData, Hadoop Hive, etc.
• Support from research till final MaaS solution deployment.

Incentivized

• Not as great as RStudio; lacks some features when compared with it
• It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product

Incentivized

• Its community support is very limited at the moment
• Complex to integrate with automation tools such as Blue Prism

Incentivized

As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

Incentivized

It is expensive and difficult to install and maintain.

Incentivized

The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.

Incentivized

Since our organization had already implemented Cloudera Data Platform as our Big Data Warehouse platform, implementing CDSW as the go-to Analytic and Data Science Platform is the most logical and cost-effective decision to make. It integrates seamlessly with our CDH clusters and it also provides enterprise-grade security for on-premise implementation.

Incentivized

Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

Incentivized

• Paid off for demonstration purposes.

Incentivized

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized