Introduction DAX

1. SQL Saturday #264
Ancona, Italy
Introduction DAX

2. Sponsors & Media Partners

3. Marco Pozzan
 Work with SQL server from 2000 version
 Work in BI since 2005
 President of the community 1nn0va
(www.innovazionefvg.net)
 Project manager at Servizi CGN (www.cgn.it)
 SQL Server and DWH Consultant
 References:
 twitter: @marcopozzan
 email: info@marcopozzan.it
 site: www.marcopozzan.it

4. Agenda
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What is Powerpivot?
Demo of Powerpivot
What is DAX?
Calculated columns and Calculated Fields
Evaluation Context
Calculate
Demo in DAX

5. What is Powerpivot?




Free AddIn for Microsoft Excel 2010 e 2013
Different versions for 32/64 bit (4GB limit)
Do not need SQL Server or other prerequisities
Very powerful analysis engine based on SSAS
di SQL Server 2012
 No API available to control it
 No security available
 Always impersonates current user

6. Versions
 installed:
 Client-side: inside to Excel
 Server-side: Built on SharePoint 2012 o SQL
Server 2012(Tabular)
 the SSAS engine client-side version the engine runs
in-process with Excel

7. SSAS 2012
 Use Vertipaq engine which is a columnar
database high compression
 Work completely in memory
 No I/O, aggregates, or other…
 IMBI = New way of thinking about the
algorithms

8. Powerpivot
 Import data
 Relationship between tables
 Slicer

9. Advantages (PowerPivot)
 Fast
 No ETL (Power Query)
 Metadata (model)
 Integration of heterogeneous sources
 Sharing
 Especially with Sharepoint
 Expressiveness
 Relation and Dax

10. Disadvantages (PowerPivot)
 There are ETL to clean the data
 Quality of data?
 Data size
 Please note that these are not problems!

11. What is DAX?
 Designed to work within a PivotTable
 Programming language of Tabular and
PowerPivot
 Resembles Excel (say )
 No concept of «row» and «column»
 Different Type System
 Mix between MDX, SQL, EXCEL

12. Dax Types
 Non numerical:
 String
 Binary Objects (Power View)
 Numerical:




Currency
Integer
Real
DateTime
 (integer: dd 30/12/1899, decimal: fraction of a day)
 Boolean

13. Type Handling
 Operators are not strongly typed ("1"+1)
 Operator Overloading (warning )
 Example
 1 & 2 = "12"
 "1" + "2" = 3

14. Columns in DAX 1/2
 'TableName’[ColumnName]
=FactInternetSales[OrderDate]
 Quotes can be omitted if the tablename does
not contain spaces (Don’t do it )

15. Columns in DAX 2/2
 TableName can be omitted and then will look
in the current table
 not to do it as it is hard to understand the
formulas
=[OrderDate]
 Brackets cannot be omitted

16. Calculated Columns
 Computed using DAX and persisted in the
database
 Use another columns
 Always computed for the current row
 FactInternetSales[OrderDate] means




The value of the OrderDate column
In the FactInternetSales table
For the current row
Different for each row

17. Measures (Calculated Fields)

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
Do not work row by row
Written using DAX
Not stored on the database
Use tables and aggregators
Do not have the «current row»
 I can not write the following formula
=FactInternetSales[OrderDate]
:=SUM(FactInternetSales[OrderDate])

18. Define the right name of the column
 If you change the name of the columns must
be changed manually in the measures
 So, immediately defined the right names

19. Calculated column e Measures
 Suppose you want to calculate the margin
with a calculated column:
=FactInternetSales[SalesAmount]-FactInternetSales[TotalProductCost]
 I Can aggregate margin column with a
measure
SUMofMargin:=SUM(FactInternetSales[Margin])

20. Calculated column e Measures
 margin compared to sales (margine%)
=FactInternetSales[Margin] / FactInternetSales[SalesAmount]
 This expression is not correct if he come
aggregate
 I must use this
Margine%:=SUM(FactInternetSales[Margin]) /
SUM(FactInternetSales[SalesAmount])

21. Measures rules and convention
 Define the name of the table to which it
belongs
 The measures are global to the model
 There may be two measures with the same name
in different tables
 You can move from one table to another, this can
not be done with computed columns
 Do not refer to a measure with table name is
confused with calculated columns

22. Summary 1/2
 Columns consume memory and measures
consume CPU
 Are calculated at different times
 They have different purposes
 They are structured differently
 Are managed in different ways

23. Summary 2/2
 Use measures (90%)
 Calculate ratios
 Calculate percentages
 Need complex aggregations
 Use column when (10%)
 It requires slicer or filter values
 The expression is calculated on the current row

24. Counting Values

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
COUNTROWS: rows in a table
COUNTBLANK: counts blanks
COUNTA: counts anything but not blanks
COUNT : only for numeric columns
 Compatibility with Excel
 DISTINCTCOUNT: performs distinct count
 Multidimensional -> measure group with a
distinctcount measure .. Slow like a snail 

25. Errors in DAX 1/2
 1+2 always works
 [SalesAmount]/[Margin] might fail
 Causes of errors
 Conversion errors
 Arithmetical operations
 Empty or missing values
 ISERROR (Expression) returns true or
false, depending on the presence of an error
during evaluation

26. Errors in DAX 2/2
 IFERROR (Expression, Alternative) in case of
error returns Alternative true. Useful to avoid
writing expression twice
 Both IFERROR and ISERROR are very slow
so be careful how you use computed
columns 

27. Aggregation Functions
 Work only on numeric columns
 Aggregation functions:

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
SUM
AVERAGE
MIN
MAX
 Aggregate columns only not expression
 SUM(Order[Quantity]) 
 SUM(Order[Quantity]) * Orders[Quantity]) 

28. The X aggregation functions 1/2
 Iterate on the table and evaluate the
expression for each row
 Always get two parameters: the table to
iterate and the formula to evaluate
 SUMX,AVERAGEX,MINX,MAXX
SUMX (
Sales,
Sales[Price] * Sales[Quantity]
)

29. The X aggregation functions 2/2
 First calculate the internal parameters and
then makes the sum
 The columns must all be on the same table
or use RELATED (if there is a relationship)
 They are very slow but I do not use memory

30. Alternatively, the X functions
 An alternative to the X functions.
 create a calculated column
 aggregate on that column
 very fast but use memory

31. Logical Functions

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AND (little used) or &&
OR (little used) or ||
IF
IFERROR
NOT (little used)
SWITCH

32. Switch
Color :=
IF(DimProduct[Color] = "R", "Red",
IF(DimProduct[Color] = "Y", " Yellow ", "Other"))
Color :=
Switch (
DimProduct[Color],
"R", "Red",
"Y", "Yellow",
"Other"
)

33. Information Function
 Completely useless (do not take the
expressions but only columns)
 ISNUMBER
 ISTEXT
 ISNONTEXT
 Useful
 ISBLANK
 ISERROR
But if we do not know us (we created the
column) if it is a number or a text those who
should know (by Alberto Ferrari)

34. DIVIDE Function
 check that the denominator is not 0
 IF( Sales[Price] <> 0, Sales[Quantity] /
Sales[Price],0)
 DIVIDE(Sales[Quantity], Sales[Price],0)

35. Date Function
 Many useful functions:
DATE ,DATEVALUE, DAY, EDATE,EMONTH
,HOUR, MINUTE, MONTH, NOW, SECOND, TI
ME, TIMEVALUE, TODAY
(interesting!!!), WEEKDAY, WEEKNUM, YEAR,
YEARFRANC
 Time intelligence functions

36. Evaluation Context 1/3
 Characterizes DAX from any other language
 They are similar to the “where clause” of the
MDX query in SSAS
 Contexts under which a formula is evaluated
 Filter Context , RowContext

37. Evaluation context 2/3
 Filter Context:
 Set of active rows for the computation
 The filter that comes from the PivotTable
 Defined by slicers, filters, columns, rows
 One for each cell of the PivotTable

38. Evaluation context 3/3
 Row Context:
 Contains a singles row
 Current row during iterations
 Define by X function or Calculate column
definition not by pivot tables
 This concept is new among MDX
 because not working leaf by leaf, but only on the
context.

39. The two context are always
 Filter context:




Filter tables
Might be empty (All the tables are visible)
It is used by aggregate functions
In calculated column is all the tables because
there is not pivot table
 Row context:
 Iterate the active row in the filter context
 Might be empty (There is no iteration running)

40. With more tables?

41. Evaluation Context
 Filter context:
 Is propagated through relationships from one to many
 The direction of the relationships is very important. Is
different from SQL (inner,left,...)
 Applies only once (+ performance)
 Row context:
 Does not propagate over relationships
 Use RELATED (open a new row context on the
target)
 Apply for each row (- performance)

42. Exmple of a Filter Context

43. Table Function
 FILTER (adding new conditons. Is an iterator!!!)
 ALL
 (Remove all conditions from a table. Returns all rows
from a table)
 Useful to calculate ratios and percentages
 Removes all filters from the specified columns in the
table
 VALUES (valori di una colonna compresi i blank)
 RELATEDTABLE (tutti i valori collegati alla riga corrente)
 All function returns a table

44. Filter

45. All

46. Mixing Filters

47. VALUES
 Return to the table with a single column
containing all possible values ​of the column
visible in the current context
SelectedYear:=COUNTROWS(VALUES(Dati[Year]))
 When the result is a column and a row can
be used as scalar

48. RELATEDTABLE
 Return only row of sales (Dati) related with
the current store (Store)
=COUNTROWS (RELATEDTABLE(Dati))

49. Considerations
 we have seen that we can:
 Can add a filter on a column
 Remove filter on the full table
 Mixing filter
 …..but:
 ignore only a part of the filter context and not all
 add a condition to the context filter or modify an
existing condition

50. Calculate
 The most simple but complex to understand
 CALCULATE(
Expression,
Filter1,
….
FiltroN
)
 Computed before the filter (AND) and then the
expression
 All filters are processed in parallel and are
independent of each other
 Replace the filter context (replace whole table
or a single column)

51. Calculate

52. Calculate with filter
 So this formula is not correct
ProductLMC :=
CALCULATE(
SUM(FactInternetSales[SalesAmount]);
DimProduct[ListPrice] > DimProduct[StandardCost] ))
 Use FILTER
ProductLMC :=
CALCULATE(
SUM(FactInternetSales[SalesAmount]);
FILTER(DimProduct,
DimProduct[ListPrice] > DimProduct[StandardCost] ))
The filter is a boolean condition
that works on a single column
(Ex: DimProduct[Color] = "White"
or DimProduct[ListPrice] > 1000)
In this case there are too many
columns in the filter (ListPrice and
StandardCost)

53. Calculate – pay attention to the filter context
ProductM100:=
CALCULATE (
SUM(FactInternetSales[SalesAmount]),
FILTER(
DimProduct,
DimProduct[ListPrice] >= 100
)
)
Color = silver
Color = silver
ListPrice >= 100
Filter Context
 The DimProduct is evaluated in the original filter
context before evaluate CALCULATE

54. Calculate – pay attention to the context filter
ProductM100_Bis:=
CALCULATE (
SUM(FactInternetSales[SalesAmount]),
FILTER(
ALL(DimProduct),
DimProduct[ListPrice] >= 100
)
)
Color = silver
ListPrice >= 100
All column …..
Filter Context
 The new context of filter will be the SUM are all the
row because “color = silver” was removed 

55. Earlier
 Returns a value from the previous row
context:
=SUMX(
FILTER(Sales;
Sales[Date]<=EARLIER(Sales[Date]) &&
YEAR(Sales[Date]) = YEAR(EARLIER(Sales[Date]))
)
;Sales[Value]
)
In row contex we have only 1 variables available
FOR A = 1 TO 5
FOR B = 1 TO 5
IF A < B THEN
NEXT
NEXT
FOR = 1 TO 5
FOR A = 1 TO 5
IF IEARLEIER ( ) < A THEN
NEXT
NEXT

56. Calculate – Context transition
 In DimProduct the two expressions are the same?
= SUM(FactInternetSales[SalesAmount]);
= CALCULATE(SUM(FactInternetSales[SalesAmount]));

57. ABC and Pareto Analysis
 80% of effects come from 20% of the causes
 L’80% of sales come from 20% of customers
 Pareto analysis is the basis of the classification
ABC
 Class A contains items for >=70% of total value
 Class B contains items for >=20% and <70% of total value
 Class C contains items for <20% of total value

58. ABC and Pareto Analysis
 For each row calculate the TotalSales
=CALCULATE( SUM(FactInternetSales[SalesAmount]))
 Calculate all products with total sales greater
than the selling of the row RunningTotalSales
= SUMX(
FILTER(
DimProduct;
DimProduct[TotalSales] >=
EARLIER(DimProduct[TotalSales])
);
DimProduct[TotalSales]
)

59. Analisi di Pareto e l’analisi ABC
 calculate the percentage of sales by product
of the total sales
=DimProduct[RunningTotalSales] / SUM(DimProduct[TotalSales])
 visualize the labels A, B, C
=IF(
DimProduct[RunningPct] <= 0.7;
"A";
IF(
DimProduct[RunningPct] < =0.9;
"B";
"C";
)
)

60. ABC and Pareto Analysis
 the number of products that generate those
sales
=COUNTROWS(DimProduct)

61. ABC and Pareto Analysis

62. Link and Book
 PowerPivot
http://www.powerpivot.com
 SQLBI
http://www.sqlbi.com
 WebCast (Powerpivot 1.0)
http://www.presentation.ialweb.it/p29261115/
 Book

63. Q&A

64. #sqlsat264
#sqlsatancona
Thanks!