Dataiku vs. Redash

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

Redash is well suited to situations where metrics are tracked on daily, weekly and monthly basis. Alerts can be set to emails which helps stakeholders to monitor performance on a frequent basis. It is less appropriate for cases where only dashboards are needed. Redash comes into picture where individuals can query and check data at the same time.

• 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

• Great Query Editor with Autocomplete feature
• Very easy to setup and quickly connect to variety of data sources
• Quick Dashboards with Simple UI which can be easily shareable

Incentivized

• End product deployment.

Incentivized

• You need to have a good command over SQL to use Redash but if there could be some way where people can just pull data and do slice dice.
• It would be nice to have an excel kind of filters when all data is fetched.
• Some things like easy to customise the column names.

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

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

I was not a part of the decision-making team who decided to go with Redash.

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

• Cost effective
• One tool for multiple purpose
• Easy access provision