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

source

ingest

process

store

deliver


 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.


Object Level Security and Power BI datasets

Object Level Security (OLS) helps meet the most demanding requirements to protect and secure your valuable data assets. In addition to Row Level Security (RLS) that helps restrict access to the rows filtered by DAX statements, OLS prevents access to columns or even complete tables.

OLS is introduced to Power BI datasets in February 2021 as a preview feature. If you require OLS in a production environment, you might consider using Azure Analysis Service (AAS). AAS supports OLS for a long time in production environments

At the time of this writing (February 2021), the user interface (UI) of Power BI Desktop does not support OLS configuration. Instead, we can use Tabular Editor to configure OLS as mentioned in the article that announced the availability of OLS in preview

Announcing public preview of Object-Level Security in Power BI | Microsoft Power BI Blog | Microsoft Power BI

The most important thing about OLS is the simple fact that it is possible to apply OLS to a table and a single column.

The reason why I'm advocating the separation of workspaces (the data workspace and content workspace), both types of security RLS and OLS, will be honored during content creation if the report creator does not belong to the data workspace.

The data model

To demonstrate OLS, I use a very simple data model build by three tables. A simple calendar table, an even more simple dimension table, and of course, a fact table. The next picture shows this data model:

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