Power BI Modelling & Data Prep training course

Data Modelling

The importance of Data Modelling: good models vs bad models

Optimal model: Star model

Denormalised model

Normalization vs Denormalization

Extreme Denormalization: working with one table

Finding balance between Normalised and “one Table schema”

Dimension tables

Fact table

Granularity

Guidelines for Selecting Fact Table Granularity

Conformed dimensions

Data dimension

Common scenarios:

                Double fact table

                Multiple Fact table

                Multiple Date Dimensions

                Many to Many relationship

                Handle events spreading in time

Data Cleansing and preparation

Import data from different sources (excel, SQL, Web etc) into Query Editor

Cleanse data and apply transformation to remove common problems:

                Deduplicate

                Remove nulls

                Unpivot tables

                Create new columns

                Create conditional columns

Combine tables:

                Append

                Merge

Introduction to M query language

                Advance Editor Language structure

                Create new query

                Create M function

Create and use Dataflow:

                Pro and cons of using Dataflow

Create a dataflow

                Cleanse data using Dataflow

                Connect PowerBi Desktop to dataflow