Redash vs. Shiny

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.

Shiny is well suited where an organisation is looking to empower their analysts to minimise time spent on repetitive analysis by deploying repeatable analytical pipelines, but also looking for them to add greater value to the organisation by utilising more advanced analytical techniques. Ideally it is well suited where IT are on board and supportive of some of the more advanced features such as deploying R Shiny dashboards.

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

• Data tables are appealing to look at.
• Enables us to create trend indexes in an effective way.
• Easy to integrate with the rest of my R syntax.

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

• Easier ways to connect to data sources
• Better access control for different roles in the organization
• Video material that allows a better learning experience

Incentivized

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

Incentivized

- Faster response working with a large amount of data. - R Studio connection and flexibility. - Scenarios modelling.

Incentivized

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

• We saw a good involvement to researchers when showing their models in shiny.
• We can have a quicker review from the user when the model is in production.
• False positives can be found easily and they help the retraining of the model.

Incentivized