This slides explains the data model in Power BI. It covers right from Power Query and DAX and some Performance points while building the Model

1. CPBIG Local Meet – February 2019 Chennai Power BI – February 2019
PASS
Understanding the Power BI Data Model
Hariharan Rajendran
Microsoft Certified Trainer

2. Chennai Power BI
About me
Microsoft Certified Trainer.
8+ years experience.
Experience in Business Intelligence
• Microsoft BI
• SAP Business Objects
Expertise in Microsoft Technologies. http://dataap.org/author/hariharanr/
@imhariharanr
hariharan-r-12635640

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Power BI Model
Introduced in 2010 – Power Pivot for Excel
OLAP Experience
In-Memory column store database (Vertipaq)
Powerful Analytical formula language (DAX)
Power Query – 2013
Power BI, Excel & Analysis Service (Azure)

4. Chennai Power BI
Why Power BI Model?
Interactive analytics at the speed of thought
• Slice and dice without writing queries
• Perform business calculations
• Support for high concurrency to support enterprise reporting
• Easy and expressive KPIs, Hierarchies, drill through, naming & translations
• Security to the data

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Architecture
Power
Query
(M)
Data Access Vertipaq DQ
Business Logics &
Queries
DAX
Data Model Tabular
BI Semantic Model
Data Sources

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Power Query
Connectivity to a wide range of data sources
Highly interactive and intuitive experience for rapid and
interactive construction for queries over any data source
and any size.
Joins across different data sources
Easy and Interactive UI
Rich Language

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M Language
Evaluation flows from one step to next step
Results of previous step used in the next step
Each step is stored as variable and reused later steps
M has Lazy Evaluations.
let
Source = Sql.Databases("."),
AdventureWorksDW2014 = Source{[Name="AdventureWorksDW2014"]}[Data],
dbo_DimProduct = AdventureWorksDW2014{[Schema="dbo",Item="DimProduct"]}[Data],
#"Filtered Rows" = Table.SelectRows(dbo_DimProduct, each ([Color] <> "NA")),
#"Capitalized Each Word" = Table.TransformColumns(#"Filtered Rows",{{"Color", Text.Proper, type text}})
in
#"Capitalized Each Word”

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Query Folding
Power Query pushes work back to the source system
whenever it can.
select [_].[ProductSubcategoryKey],
[_].[ProductSubcategoryAlternateKey],
[_].[EnglishProductSubcategoryName],
[_].[ProductCategoryKey]
from
(
select [ProductSubcategoryKey],
[ProductSubcategoryAlternateKey],
[EnglishProductSubcategoryName],
[ProductCategoryKey]
from [dbo].[DimProductSubcategory] as [$Table]
) as [_]
where ([_].[EnglishProductSubcategoryName] = 'Mountain Bikes' or [_].[EnglishProductSubcategoryName] = 'Road Bikes’)
or [_].[EnglishProductSubcategoryName] = 'Touring Bikes'

9. Chennai Power BI
Data Storage
Data is either stored in memory (Vertipaq) or stored at the
data source level (DirectQuery).
In memory is fast and interactive build for analytics
Column based compression.
Direct Query is always up to date, it relies on data source
performance
Vertipaq – Performance
• Query Performance > Processing performance

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How does data get loaded?
Table 1
Table data stored in segments & dictionaries per columns
Calculated columns are stored like regular columns
Hierarchies
Relationship structures
Table 2

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Relationships

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Relationships
Data is always filtered from 1 to M side.
This works great in star schema
What if we don’t have a star schema? Or want to count
values in dimension?
In each country, how many customers bought bikes?
Bi-directional relationship

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Demo

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DAX
Introduced as part of Power Pivot – add-in for excel
Formula for calculated columns, measures and tables
Navigate relationships
Dynamic calculation of measures
Table valued functions
Time Intelligence
Variables

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Purpose of DAX
Calculated Columns
• Value is computed for each row
• Materialized in tables as a static column – It consumes memory
• It can be used for slicers and filters
• All rows contain same formula.
Calculated Measures
• Numeric calculation
• Not Materialized
• Dynamic – Does not consume memory
• Evaluated for each row based on the given context.

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Navigation Functions
RELATED
• Follows the relationships and returns the
value of a column
• Single value as a result
RELATEDTABLE
• Follows relationships and returns all the rows
in relationship with the current row value.

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Evaluation Contexts
Row Context Filter Context

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Demo

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Direct Query
Don’t want to wait for data to load. Need real-time data
Performance - Query patterns
Concurrency
Security

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Direct Query
Use an optimized data source if possible
Optimize for analytics
• Systems should handle querying of dimensions and facts
• Analytical ready data source helps
• SQL Server in Memory
• SAP HANA
Use Bi-directional cross filtering
Understand security requirements
RLS is very expensive

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Direct Query
Assume referential integrity

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Direct Query
Query Reduction

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Resources
CPBIG Webinar Recordings -
http://dataap.org/cpbig/webinar-recordings/
SQL Server, Power BI & AI - http://dataap.org/blog/
Power BI User Group - Chennai

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Thank You - நன்றி