Data exploration - Where my data engineers at
Telecom network engineer here, I got pigeon holed into data engineering responsibilities because as far as I am aware we don't have those. What are your favorite tools/ best practices for ripping into new data sets? Identifying underlying causality? We recently were provided Tableau licenses, which work well enough, but I constantly run into scalability issues. Personally I prefer complete joining complete tables and pivoting/ visualizing them until I find underlying nuances, but seem to run face first into resource issues constantly. Interested to hear others thoughts and opinions!
looker is on the way to a leading data platform
thanks, I'll give it a look!
I think we have an update coming you may like :p
hyperhyper
I was referring to maestro but yeah... Hyper hyper!!
Depends on what you want. There is some talk I've seen about Dash - which is sort of like Shiny for Python. I haven't tried it yet but it might be something to look into for you.
I really like adding 5-6 core pivots then adding 10-20 slicers to see how the distributions changes given different filter combinations. the main platform that I have ever used that delivers something close is Salesforce Wave. basically being able to scope in and out with dynamic specificity without having to manually build tables and rip them down over and over, but on a scale of 2-5 huge sets
I feel like this is the first non negative/troll thread I've seen on blind :P
There be smart people here, why not use them for their brains imo :P
Tableau is really hard to beat for rapid, beautiful vis. If you want something the can scale at the cost of visual appeal, python is my go to, particularly if you have a spark cluster.
I will definitely look into that, thanks!
Try Databricks community edition. Free spark cluster