Understanding Power BI Data Model
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The document outlines a presentation on Power BI, led by Hariharan Rajendran in Chennai, covering its architecture, data modeling, and features like Power Query and DAX. It emphasizes the benefits of Power BI for interactive analytics and data handling, including in-memory storage and direct query options. The presentation aims to educate users on efficiently utilizing Power BI for data analysis and reporting.
![Chennai Power BI
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”](https://image.slidesharecdn.com/03understandingpowerbidatamodel-190225043901/85/Understanding-Power-BI-Data-Model-7-320.jpg)
![Chennai Power BI
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'](https://image.slidesharecdn.com/03understandingpowerbidatamodel-190225043901/85/Understanding-Power-BI-Data-Model-8-320.jpg)
Understanding Power BI Data 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
- 3. Chennai Power BI 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
- 5. Chennai Power BI Architecture Power Query (M) Data Access Vertipaq DQ Business Logics & Queries DAX Data Model Tabular BI Semantic Model Data Sources
- 6. Chennai Power BI 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
- 7. Chennai Power BI 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”
- 8. Chennai Power BI 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
- 10. Chennai Power BI 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
- 12. Chennai Power BI 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
- 14. Chennai Power BI 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
- 15. Chennai Power BI 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.
- 16. Chennai Power BI 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.
- 17. Chennai Power BI Evaluation Contexts Row Context Filter Context
- 19. Chennai Power BI Direct Query Don’t want to wait for data to load. Need real-time data Performance - Query patterns Concurrency Security
- 20. Chennai Power BI 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
- 21. Chennai Power BI Direct Query Assume referential integrity
- 22. Chennai Power BI Direct Query Query Reduction
- 23. Chennai Power BI Resources CPBIG Webinar Recordings - http://dataap.org/cpbig/webinar-recordings/ SQL Server, Power BI & AI - http://dataap.org/blog/ Power BI User Group - Chennai
- 24. Chennai Power BI
- 25. Chennai Power BI Thank You - நன்றி
Editor's Notes
- #16: 1. Sales Amount -- computed for row by row -- Memory -- Product[Price] The value of the price column from the product table for the current row Different for each row 2. Profit Margin -- Calculated column -- Calculated measure
- #18: http://sqlml.azurewebsites.net/2017/04/16/evaluation-contexts-in-dax/