ianformanek

Pilgrims by Amanita Design

There might be several decent ways to improve (at least as a next step) to avoid this particular way to game the KPIs: - use an assessed upfront difficulty (if available in measurable form) to adjust for the outcome rating - add additional KPI for % of refused operations (perhaps again adjusted for difficulty)

Very good points.

Re: the R output - for us the R script is an optional step on the data pipeline, it can be either of these 2:

SQL Query -> (data table) -> visualisation SQL Query -> (data table) -> R script -> (data table) -> visualisation

i.e. the R step is injected, takes data as input and produces data (in the form of data.frame, vector of values or primitive values) as output.

Re: the filters. We support time-based filters very well with a nice UI (custom time ranges, time ranges relative to today, additional comparison time range). Custom value filters are currently in beta and once launched (in couple weeks) will support multi-select as well as values based on dynamic queries.

Right, this would benefit from being a bit clearer.

Our backend query execution service connects to the customer's own DB as a client. The connection can be secured via SSH tunnel and firewall rules (for our NAT gateway IP addresses) - that way the database would never have to be exposed to the internet (as it never should be).

I am not too familiar with all capabilities of the Google Data Studio, but from what I know they take a bit more the Tableau approach to defining data sources as results of queries (BigData, MySQL) or sheets data.

I'd say Cluvio would be a bit more fun to use for anyone who knows SQL, the ability to quickly iterate through sql queries to nail down the exact results is quite addictive. Plus the additional capabilities of R make it even more productive for lots of harder cases.

At the moment we fully support Postgres, AWS Redshift, MySQL/MariaDB, AWS Aurora. More to come as we see demand (comment here please!)

There are some similarities conceptually (SQL-based analytics, use of R or Python to give additional capabilities).

There are more differences in the visualisation capabilities, user experience and pricing (esp. with larger number of business users that should make use of the analytics results, which are free in our case).

Thanks for the report - I actually noticed from spell checker (not being a native speaker) the other day that 'almanach' is not the most common spelling, but it is listed as an alternate spelling on wikipedia, so unless it obviously comes across as a typo we'd keep it (to perhaps make it sound more like a DnD spellbook ;-) )

Hi there, Ian here, the other founder of Cluvio.

There are some limitations to the R execution environment, as you correctly expect, as a result of running custom code in the hosted environment.

Our use case for R is to provide additional expressivity for things that are harder to do in SQL and very easy in R, as part of the data pipeline. We do not allow to run some system calls and do not allow to install additional packages.

Having said that, we do currently include some good basis with plyr, dplyr, reshape, and more will be added based on demand.