Index

A

ABC analysis (segmentation), 196–200 active events (duration), 137–146 additive measures

aggregating snapshots, 114–117 fact tables, 51

overview, 225 aggregating

detail tables, 24–30 duration, 129–131 header tables, 24–30 snapshots, 112–117

additive measures, 114–117 semi-additive measures, 114–117

ALL function, 40

allocation factor (granularity), 185–186 ambiguity (relationships), 17–19, 43–45 automatic time dimensions

creating, 58–60 Excel, 58–59

Power BI Desktop, 60

B

bidirectional filtering (cross-filtering)

CROSSFILTER function, 43, 52, 98, 156–157, 159–160, 168, 220 detail tables, 25–29

fact tables, 40–43 granularity, 179–181 header tables, 25–29

many-to-many relationships, 155–157 overview, 218–221

BLANK function, 182

bridge tables defined, 222

many-to-many relationships, 167–170 orders and invoices example, 52–53 overview, 224

budgets (granularity), 175–177

C

CALCULATE function, 37, 43, 77, 124, 156, 181, 193, 220 calculated columns (segmentation), 196–200 CALCULATETABLE function, 81, 124

calculating

CALCULATE function, 37, 43, 77, 124, 156, 181, 193, 220 calculated columns (segmentation), 196–200 CALCULATETABLE function, 81, 124

time intelligence, 68–69 calendars

fiscal calendars, 69–71 weekly calendars, 84–89

cascading many-to-many relationships, 158–161 CLOSINGBALANCELASTQUARTER function, 226 columns

foreign keys, defined, 9 names, 20–21

primary keys, defined, 8–9 segmentation

calculated columns, 196–200 multiple-column relationships, 189–192

CONTAINS function, 178 converting currency

multiple reporting currencies multiple source currencies, 212–214 single source currency, 208–212

multiple source currencies

multiple reporting currencies, 212–214

single reporting currency, 204–208 overview, 203–204

single source currency, multiple reporting currencies, 208–212 single reporting currency, multiple source currencies, 204–208

COUNTROWS function, 75, 97, 220 creating

automatic time dimensions, 58–60 date dimensions, 55–58

CROSSFILTER function, 43, 52, 98, 156–157, 159–160, 168, 220 cross-filtering (bidirectional filtering)

CROSSFILTER function, 43, 52, 98, 156–157, 159–160, 168, 220 detail tables, 25–29

fact tables, 40–43 granularity, 179–181 header tables, 25–29

many-to-many relationships, 155–157 overview, 218–221

currency conversion

multiple reporting currencies multiple source currencies, 212–214 single source currency, 208–212

multiple source currencies

multiple reporting currencies, 212–214 single reporting currency, 204–208

overview, 203–204

single source currency, multiple reporting currencies, 208–212 single reporting currency, multiple source currencies, 204–208

D

data

models. See data models temporal data. See duration types. See data types

data models

denormalization, 13–15, 18–19

detail tables aggregating, 24–30

bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

flattening, 30–32 foreign keys, 9

granularity. See granularity header tables

aggregating, 24–30 bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

many-to-many relationships. See many-to-many relationships normalization, 12–15, 18–19

OLTP, 13–15 one-to-many relationships

defined, 9

fact tables, 47–49 primary keys, 8–9

relationships. See relationships segmentation

ABC analysis, 196–200 calculated columns, 196–200 dynamic, 194–196

multiple-column relationships, 189–192 overview, 189

static, 192–193 single tables, 2–7

snowflake schemas, overview, 18–19, 222–223 source tables, 9

star schemas, overview, 15–19, 222 tables. See tables

targets, 9

data types overview, 217 date. See also time

calendars

fiscal calendars, 69–71 weekly calendars, 84–89

CLOSINGBALANCELASTQUARTER function, 226 date dimensions

creating, 55–58 multiple, 61–66

using with time dimensions, 66–68 DATESINPERIOD function, 69 DATESYTD function, 68, 70–71, 226 duration

active events, 137–146 aggregating, 129–131 formula engine, 141 mixing durations, 146–150 multiple dates, 131–135 overview, 127–129

temporal many-to-many relationships, 161–167 time shifting, 135–136

LASTDATE function, 114–115, 141, 226 LASTDAY function, 68

periods

DATESINPERIOD function, 69 non-overlapping, 79–80 overlapping, 82–84

overview, 78

PARALLELPERIOD function, 68 relative to today, 80–82

SAMEPERIODLASTYEAR function, 68, 87 SAMEPERIODLASTYEAR function, 68, 87 separating from time, 67, 131

TOTALYTD function, 226 working days

multiple countries, 74–77 overview, 72

single countries, 72–74 date dimensions

creating, 55–58 multiple, 61–66

using with time dimensions, 66–68 DATESINPERIOD function, 69 DATESYTD function, 68, 70–71, 226 denormalization. See also normalization

data models, 13–15, 18–19 fact tables, 35–40 flattening, 30–32

derived snapshots defined, 112 overview, 118–119

detail tables aggregating, 24–30

bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

diagram (relationship diagram), 18 dimensions

ambiguity, 17–19, 43–45

automatic time dimensions creating, 58–60

Excel, 58–59

Power BI Desktop, 60 date dimensions

creating, 55–58 multiple, 61–66

using with time dimensions, 66–68 defined, 15, 222

detail tables, 23–24 fact tables

bidirectional filtering, 40–43 multiple dimensions, 35–40

header tables, 23–24 names, 20–21

rapidly changing dimensions, 106–109 relationships, 17–19

SCDs (slowly changing dimensions) dimensions, 102–104

fact tables, 104–106 granularity, 102–106 loading, 99–106 overview, 91–95

rapidly changing dimensions, 106–109 techniques, 109–110

types, 92 using, 96–99 versions, 96–99

time dimensions, 66–68 displaying

Power Pivot (Ribbon), 10 relationship diagram, 18 values (granularity), 181–185

DISTINCT function, 40 DISTINCTCOUNT function, 96–97

duration

active events, 137–146 aggregating, 129–131 formula engine, 141 mixing durations, 146–150 multiple dates, 131–135 overview, 127–129

temporal many-to-many relationships, 161–167 materializing, 166–167

reallocation factors, 164–166 time shifting, 135–136

dynamic segmentation, 194–196

E

events (active events), 137–146 examples (orders and invoices) additive measures, 51

bridge tables, 52–53 detail tables, 49–50 fact tables, 45–53 header tables, 49–50

many-to-many relationships, 47, 52 one-to-many relationships, 47–49

Excel

automatic time dimensions, 58–59

Power BI Desktop, automatic time dimensions, 60 Power Pivot, viewing, 10

EXCEPT function, 77–78

F

fact tables

ambiguity, 17–19, 43–45 defined, 15, 221 denormalization, 35–40 detail tables

aggregating, 24–30 bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

dimensions

bidirectional filtering, 40–43 detail tables, 23–24

header tables, 23–24 multiple dimensions, 35–40

header tables aggregating, 24–30

bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

names, 20–21

orders and invoices example, 45–53 additive measures, 51

bridge tables, 52–53 detail tables, 49–50 header tables, 49–50

many-to-many relationships, 47, 52 one-to-many relationships, 47–49

overview, 35 relationships, 17–19

SCDs (granularity), 104–106 FILTER function, 148, 181, 193, 226 filtering

bidirectional filtering (cross-filtering)

CROSSFILTER function, 43, 52, 98, 156–157, 159–160, 168, 220 detail tables, 25–29

fact tables, 40–43 granularity, 179–181 header tables, 25–29

many-to-many relationships, 155–157 overview, 218–221

FILTER function, 148, 181, 193, 226 moving filters (granularity), 177–179 overview, 218–221

fiscal calendars, 69–71 flattening, 30–32 foreign keys, defined, 9

formula engine (duration), 141 functions

ALL, 40 BLANK, 182

CALCULATE, 37, 43, 77, 124, 156, 181, 193, 220 CALCULATETABLE, 81, 124 CLOSINGBALANCELASTQUARTER, 226 CONTAINS, 178

COUNTROWS, 75, 97, 220

CROSSFILTER, 43, 52, 98, 156–157, 159–160, 168, 220 DATESINPERIOD, 69

DATESYTD, 68, 70–71, 226 DISTINCT, 40 DISTINCTCOUNT, 96–97 EXCEPT, 77–78

FILTER, 148, 181, 193, 226

HASONEVALUE, 77, 205 IF, 76

IFERROR, 193

INTERSECT, 37–38, 124, 178–179 ISEMPTY, 193

LASTDATE, 114–115, 141, 226 LASTDAY, 68

List.Numbers, 100 LOOKUPVALUE, 81, 86, 190–191 MAX, 101, 103, 195

MIN, 195 PARALLELPERIOD, 68 RELATED, 43, 61, 74 RELATEDTABLE, 61, 74

SAMEPERIODLASTYEAR, 68, 87 SUM, 114, 134–135, 144, 155–156 SUMMARIZE, 165

SUMX, 158 TOTALYTD, 226 TREATAS, 179 UNION, 40

USERELATIONSHIP, 44, 61 VALUES, 193

G

granularity

bidirectional filtering, 179–181 budgets, 175–177

data models

multiple tables, 11–15 single tables, 4–7

detail tables, 27–29 header tables, 27–29 moving filters, 177–179 overview, 173–175 SCDs

dimensions, 102–104 fact tables, 104–106

snapshots, 117 values

allocation factor, 185–186 hiding, 181–185

H

HASONEVALUE function, 77, 205 header tables

aggregating, 24–30 bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

hiding/viewing

Power Pivot (Ribbon), 10 relationship diagram, 18 values (granularity), 181–185

hierarchies (tables), 23–24

I

IF function, 76 IFERROR function, 193

INTERSECT function, 37–38, 124, 178–179 intervals. See duration

invoices and orders example additive measures, 51 bridge tables, 52–53 detail tables, 49–50 fact tables, 45–53 header tables, 49–50

many-to-many relationships, 47, 52 one-to-many relationships, 47–49

ISEMPTY function, 193

K–L

keys, 8–9

LASTDATE function, 114–115, 141, 226 LASTDAY function, 68

List.Numbers function, 100 loading SCDs, 99–106

LOOKUPVALUE function, 81, 86, 190–191

M

many-to-many relationships bidirectional filtering, 155–157 bridge tables, 167–170 cascading, 158–161

fact tables, 47, 52

non-additive measures, 157–158 overview, 153–158 performance, 168–170

temporal many-to-many relationships, 161–167 materializing, 166–167

reallocation factors, 164–166 many-to-one relationships

defined, 9

fact tables, 47–49

materializing (many-to-many relationships), 166–167 matrixes (transition matrixes)

slicers, 123–124 snapshots, 119–125

MAX function, 101, 103, 195 measures

additive, 114–117, 225 aggregating snapshots, 114–117

many-to-many relationships, 157–158 non-additive, 157–158, 225 semi-additive, 114–117, 225–226

MIN function, 195

mixing durations, 146–150 models. See data models

moving filters (granularity), 177–179 multiple columns (segmentation), 189–192 multiple countries (working days), 74–77 multiple date dimensions, 61–66

multiple dates (duration), 131–135 multiple dimensions (fact tables), 35–40 multiple durations, mixing, 146–150 multiple reporting currencies

multiple source currencies, 212–214 single source currency, 208–212

multiple source currencies

multiple reporting currencies, 212–214 single reporting currency, 204–208

multiple tables (granularity), 11–15

N

names

columns, 20–21 dimensions, 20–21 fact tables, 20–21 objects, 20–21 tables, 20–21

natural snapshots, 112 non-additive measures

many-to-many relationships, 157–158 overview, 225

non-overlapping periods, 79–80 normalization

data models, 12–15, 18–19 denormalization

data models, 13–15, 18–19 fact tables, 35–40

flattening, 30–32

O

object names, 20–21

OLTP (online transactional processing), 13–15 one-to-many relationships

defined, 9

fact tables, 47–49

online transactional processing (OLTP), 13–15 orders and invoices example

additive measures, 51 bridge tables, 52–53 detail tables, 49–50 fact tables, 45–53 header tables, 49–50

many-to-many relationships, 47, 52 one-to-many relationships, 47–49

overlapping periods, 82–84

P

PARALLELPERIOD function, 68

performance (many-to-many relationships), 168–170 periods

dates

non-overlapping, 79–80 overlapping, 82–84 overview, 78

relative to today, 80–82 DATESINPERIOD function, 69 PARALLELPERIOD function, 68 SAMEPERIODLASTYEAR function, 68, 87

Power BI Desktop, automatic time dimensions, 60 Power Pivot, viewing, 10

primary keys, defined, 8–9

R

rapidly changing dimensions, 106–109

reallocation factors (many-to-many relationships), 164–166 RELATED function, 43, 61, 74

RELATEDTABLE function, 61, 74 relationship diagram, 18 relationships

ambiguity, 17–19, 43–45 data models, 7–15

denormalization, 13–15, 18–19 dimensions, 17–19

fact tables, 17–19 foreign keys, 9 granularity

allocation factor, 185–186 bidirectional filtering, 179–181 budgets, 175–177

hiding values, 181–185 moving filters, 177–179 multiple tables, 11–15

many-to-many relationships. See many-to-many relationships normalization, 12–15, 18–19

OLTP, 13–15 one-to-many relationships

defined, 9

fact tables, 47–49 overview, 217–218 primary keys, 8–9 relationship diagram, 18

segmentation, multiple columns, 189–192 source tables, 9

tables, 7–15 targets, 9

reporting currencies

multiple reporting currencies multiple source currencies, 212–214 single source currency, 208–212

single reporting currency, multiple source currencies, 204–208 Ribbon, viewing Power Pivot, 10

S

SAMEPERIODLASTYEAR function, 68, 87 SCDs (slowly changing dimensions)

granularity dimensions, 102–104 fact tables, 104–106

loading, 99–106 overview, 91–95

rapidly changing dimensions, 106–109 techniques, 109–110

types, 92 using, 96–99 versions, 96–99

schemas

snowflake schemas, overview, 18–19, 222–223 star schemas, overview, 15–19, 222

segmentation

ABC analysis, 196–200 calculated columns, 196–200 dynamic, 194–196

multiple-column relationships, 189–192 overview, 189

static, 192–193 semi-additive measures

aggregating snapshots, 114–117 overview, 225–226

shifting (time shifting), 135–136 showing

Power Pivot (Ribbon), 10

relationship diagram, 18 values (granularity), 181–185

single countries (working days), 72–74

single reporting currency, multiple source currencies, 204–208 single source currency, multiple reporting currencies, 208–212 single tables (data models), 2–7

size. See granularity

slicers (transition matrixes), 123–124 slowly changing dimensions (SCDs)

granularity dimensions, 102–104 fact tables, 104–106

loading, 99–106 overview, 91–95

rapidly changing dimensions, 106–109 techniques, 109–110

types, 92 using, 96–99 versions, 96–99

snapshots

additive measures, 114–117 aggregating, 112–117 derived, 112, 118–119 granularity, 117

natural, 112 overview, 111–112

semi-additive measures, 114–117 slicers, 123–124

transition matrixes, 119–125 types, 112

snowflake schemas, overview, 18–19, 222–223 source currencies

multiple source currencies

multiple reporting currencies, 212–214 single reporting currency, 204–208

single source currency, multiple reporting currencies, 208–212 source tables, defined, 9

star schemas, overview, 15–19, 222 static segmentation, 192–193

SUM function, 114, 134–135, 144, 155–156 SUMMARIZE function, 165

SUMX function, 158

T

tables

bridge tables defined, 222

many-to-many relationships, 167–170 orders and invoices example, 52–53 overview, 224

CALCULATETABLE function, 81, 124 columns

calculated columns, 196–200 foreign keys, defined, 9

multiple-column relationships, 189–192 names, 20–21

tables (continued)

primary keys, defined, 8–9 segmentation, 189–192, 196–200

denormalization, 13–15, 18–19 detail tables

aggregating, 24–30 bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

dimensions. See dimensions

fact tables. See fact tables foreign keys, 9 granularity. See granularity header tables

aggregating, 24–30 bidirectional filtering, 25–29 dimensions, 23–24 flattening, 30–32 granularity, 27–29 hierarchies, 23–24

orders and invoices example, 49–50 overview, 23–24

many-to-many relationships. See many-to-many relationships names, 20–21

normalization, 12–15, 18–19 OLTP, 13–15

one-to-many relationships defined, 9

fact tables, 47–49 overview, 215–216 primary keys, 8–9

RELATEDTABLE function, 61, 74 relationships. See relationships single tables, 2–7

snapshots

additive measures, 114–117 aggregating, 112–117 derived, 112, 118–119 granularity, 117

natural, 112 overview, 111–112

semi-additive measures, 114–117 slicers, 123–124

transition matrixes, 119–125 types, 112

source tables, 9 targets, 9 transition matrixes

slicers, 123–124 snapshots, 119–125

targets, defined, 9 techniques (SCDs), 109–110 temporal data. See duration

temporal many-to-many relationships, 161–167 materializing, 166–167

reallocation factors, 164–166 time. See also date

duration

active events, 137–146 aggregating, 129–131 formula engine, 141 mixing durations, 146–150 multiple dates, 131–135 overview, 127–129

temporal many-to-many relationships, 161–167 time shifting, 135–136

separating from date, 67, 131 time dimensions

automatic time dimensions, 58–60 using with date dimensions, 66–68 time intelligence. See time intelligence

time dimensions

automatic time dimensions creating, 58–60

Excel, 58–59

Power BI Desktop, 60

using with date dimensions, 66–68 time intelligence

calculating, 68–69 calendars

values granularity

allocation factor, 185–186 hiding, 181–185

HASONEVALUE function, 77, 205 LOOKUPVALUE function, 81, 86, 190–191 VALUES function, 193

VALUES function, 193 versions (SCDs), 96–99 viewing

Power Pivot (Ribbon), 10 relationship diagram, 18 values (granularity), 181–185

W

weekly calendars, 84–89 working days

multiple countries, 74–77 overview, 72

single countries, 72–74 working shifts. See duration