Mincing Data - Gain insight from data

This site will be a container for all my musings about the analytical pipeline.

For this reason it will be necessary to define the Analytical Pipeline (at least define this pipeline from my point of view). From a general perspective (be aware that this is my perspective) five activities are necessary to build an analytical pipeline, these steps are






 The overall goal of an analytical pipeline is to answer an analytical question. To achieve this overall goal, different data sources have to be targeted and their data has to be ingested into the pipeline and properly processed. During these first steps the data ingested into the pipeline often has to be stored in one or more different data stores, each is used or its special type of usage, finally the result of the data processing has to be delivered to its users, its audience. Depending on the nature of the question, different types of processing methods and also different data stores may be used along the flow of the data throughout the pipeline.


I put a direction to these activities, but I also added this direction to spur your critical mind, because

  • source data is of course also stored somewhere
  • to target the source can become a challenge in the pipeline
  • to deliver processed data to the its audience can become a another challenge  if you try to deliver to mobile workforce
Successfully ingested data often gets processed more than once to answer different questions, and during its existence (inside an analytical data platform) this data will be transformed into various shapes, wrangled by different algorithms, and ingested into different "data stores".

For this reason I believe that these activities are tightly related, and the above mentioned sequence of these activities will just aid as a guidance.


I will use blog posts to describe how different activities are combined to answer analytical questions. In most of my upcoming blog posts I will link to different topics from the activities used in the pipeline. Each activity has its own menu and is by itself representing an essential part in analytical pipeline.


Hopefully this site will help its readers as much as it helps me to focus on each activity always knowing that most of the time more than one activity has to be mastered to find an answer to an analytical question.

The (better) rectangular pie chart

It took me more than two years to write this article because it started back in April 2021: https://www.minceddata.info/2021/04/03/the-pineapple-pizza-debate-or-why-pie-chart-are-not-that-evil/

I learned a few things, but not all about Vega-Lite and Deneb, between then and now. You can find most of these Deneb related learnings here: https://github.com/tomatminceddata/learningdeneb

There are a lot of fancy data visualizations out there, and magical things can be done using Vega and Vega-lite, but still, my favorite is the stacked bar chart.

The below image shows what I’m talking about:

Using Deneb it’s possible to add two features of the pie chart (or donut chart) to a bar chart:

  • Ordered slices (the segments that form a stack). I consider the change in position a valuable information.
  • Contribution of segments to a given cumulative percentage. Using a pie chart it’s easy (requiring little mental effort) to spot the contributing segments until a given cumulative percentage is reached or surpassed.
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