Alteryx Designer Cloud vs. Dataiku

For doing ETL and data transformations where your data is already in a cloud environment Alteryx Designer Cloud is ideal. It allows for the continued democratisation of data and analytics to permeate more extensively throughout a business, particularly those that are already in the cloud or have cloud services in place and are comfortable with procurement and consumption of these systems over and above desktop software. The ETL processing for data validation and wrangling uses machine learning to enable highly efficient data manipulation which saves lots of time and this is a distinct advantage over the desktop software. The expansion and integration of other Alteryx cloud services such as ML, Metrics Store and App builder will only strengthen this position

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

Incentivized

• Gives automatic options for code.
• Allows for notes on code.
• Duplicates work and allows for repurposing work.

Incentivized

• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

Incentivized

• Hadoop source data tables are presented in a flat searchable list but I would rather see them in the native hierarchy.
• Web interface can be flaky. We often need to refresh the page.

Incentivized

• End product deployment.

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

The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

Incentivized

Our IT group presented Trifacta to me. They picked it out and among the crowd for me. This was way beyond ETL tools, SQL, or any programming language. It's just much faster and because it's sitting on Hadoop you bypass some of the slowdowns typically faced with RDBMS.

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

• Increased productvity by 5x
• Reduced costs of running and patching desktop and server based software entirely

• 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