Real Tableau – Data Load Monitoring
The Tableau community have recently been sharing some “real” Tableau Dashboards on Twitter using the hashtag #realTableau. The idea is to share the work we do in Tableau behind close doors. This work will often have different constraints and requirements than Tableau Public / Makeover Monday type work and so it’s important that it gets highlighted as much as the fun stuff many people do – of course the challenge is much of it is private. Thankfully many people have stepped forward to share, for example, Charlie Hutchinson has been blogging about his dashboards and you can see many more examples on the #realTableau thread.
I thought I’d take a moment to share a very old dashboard I’ve used on a few projects to analyse data loads. Each data load is being automatically loaded into a database but each row (or column in the real dashboard) might have errors or warnings relating to whether the data could be loaded.
If we need to build a a visualisation to show these loads then we’re interested in a number of things:
- how frequent are the loads per client?
- how big is each load? (we might want to spot outliers in size variation e.g. if a client regularly sends 1000 records and suddenly they send 10 records it might be an issue)
- how successful is each load?
- where in each data load are the errors occurring, is an error throughout the data or is it just on the top X rows, say?
- what specifically is the error for a given line to help us pinpoint the actual problem.
The dashboard below outlines how I approach this situation with an example of a dashboard using fake data I generated in Alteryx. The actual real visualisations included information per column in the sheet on the top right, as well as row by row information on the actual values being imported in the third (bottom right) sheet.
Using Action filters to drill into problem areas and investigate issues was a key requirement and it helped speed up the dashboard here which was hundreds of million rows in some instances. The action filters only focused on the necessary data and kept the speed down to just a second or so for querying the data. Aggregating data and providing data drill through is a key exploratory requirement in many Tableau dashboards.
Click below to explore the dashboard and download it from Tableau Public.