![let
Source = #table(
type table [CUSTOMER_ID = Int64.Type, CUSTOMER_NAME = text, CUSTOMER_CITY = text, CUSTOMER_STATE = text],
{
{1, "Bob Smith", "Rockford", "IL"},
{2, "Randy Savage", "St Louis", "MO"},
{3, "Lucy Davis", "Lansing", "MI"}
}
),
// Replaces Underscore Field Name Values and Converts to Proper Casing
ReplaceUnderscores = Table.TransformColumnNames(Source,
each Text.Proper(
Replacer.ReplaceText( _ , "_", " "
)))
in
ReplaceUnderscores
In the above let expression we transform our column names replacing underscores with a space and proper
casing with an each loop. The underscore character is used to iterate thru each item in our column headers
collection.
Full Solution: Replaces Underscores](https://image.slidesharecdn.com/itsnotyou-190423224814/85/It-s-Not-You-It-s-Your-Data-Model-8-320.jpg)
![let
Source = #table(
type table [CustomerID = Int64.Type, CustomerName = text, CustomerCity = text, CustomerState = text],
{
{1, "Bob Smith", "Rockford", "IL"},
…
}
),
/*
Splits Column Header Characters When Transitioning From Lower Case to Upper Case to a list of Values
Wrapped in a Text Combine to Combine all List Values to a Scalar Text Value
*/
SplitByCharacter = Table.TransformColumnNames(Source,
each Text.Combine(
Splitter.SplitTextByCharacterTransition({"a".."z"}, {"A".."Z"})(_)
, " ")
)
in
SplitByCharacter
In the above let expression we transform our column names splitting each text into a list by character by
transition whenever lower-cased characters in a list are followed by upper-cased characters in a list. The
split lists of text are combined including a space between each list item.
Full Solution: Split Text By Character Position](https://image.slidesharecdn.com/itsnotyou-190423224814/85/It-s-Not-You-It-s-Your-Data-Model-9-320.jpg)
It's Not You. It's Your Data Model.
- 1. Not quite what I was expecting I went ahead and built my own report instead Missed the mark PAINFULLY SLOWDifficult to navigate I’ll never use those features Something like beforeI didn’t want you to change the way it’s always been done
- 2. IT’S NOT YOU. IT’S YOUR DATA MODEL.
- 3. ALEX M POWERS Microsoft Certified Solutions Associate: BI Reporting (Excel, Power BI) Microsoft Technology Associate: Python (Introduction to Programming) Microsoft Office Expert: Excel 2013, Excel 2010 Microsoft Office Specialist: Access 2013, Excel 2010 Microsoft #HowWeExcel Contest Winner Co-Organizer of the St Louis Power BI User Group #STLPBIUG Millennial Whisperer and Trainer | It’s Not About The Cell LLC: itsnotaboutthecell.com Confused by the term “Pair of Socks” Bad at Fortnite
- 4. USER EXPERIENCE (UX) Refers to a person's emotions and attitudes about using a product, system or service. It includes the practical, experiential, affective, meaningful and valuable aspects of human–computer interaction and product ownership. Additionally, it includes a person’s perception of system aspects such as ease of use, efficiency and utility. Source (Wikipedia)
- 5. SESSION OBJECTIVES 1. Readability allows for discovery. (Ease of Use) 2. Performance considerations. (Efficiency) 3. Trust the abilities of your end users to learn. (Utility)
- 7. FIELD NAMES Type Example Camel Case orderDate Pascal Case OrderDate Snake Case ORDER_DATE
- 8. let Source = #table( type table [CUSTOMER_ID = Int64.Type, CUSTOMER_NAME = text, CUSTOMER_CITY = text, CUSTOMER_STATE = text], { {1, "Bob Smith", "Rockford", "IL"}, {2, "Randy Savage", "St Louis", "MO"}, {3, "Lucy Davis", "Lansing", "MI"} } ), // Replaces Underscore Field Name Values and Converts to Proper Casing ReplaceUnderscores = Table.TransformColumnNames(Source, each Text.Proper( Replacer.ReplaceText( _ , "_", " " ))) in ReplaceUnderscores In the above let expression we transform our column names replacing underscores with a space and proper casing with an each loop. The underscore character is used to iterate thru each item in our column headers collection. Full Solution: Replaces Underscores
- 9. let Source = #table( type table [CustomerID = Int64.Type, CustomerName = text, CustomerCity = text, CustomerState = text], { {1, "Bob Smith", "Rockford", "IL"}, … } ), /* Splits Column Header Characters When Transitioning From Lower Case to Upper Case to a list of Values Wrapped in a Text Combine to Combine all List Values to a Scalar Text Value */ SplitByCharacter = Table.TransformColumnNames(Source, each Text.Combine( Splitter.SplitTextByCharacterTransition({"a".."z"}, {"A".."Z"})(_) , " ") ) in SplitByCharacter In the above let expression we transform our column names splitting each text into a list by character by transition whenever lower-cased characters in a list are followed by upper-cased characters in a list. The split lists of text are combined including a space between each list item. Full Solution: Split Text By Character Position
- 10. PERFORMANCE BEATS PRETTY 100% OF THE TIME 50% OF THE TIME
- 11. BEHIND THE CURTAIN 1. xVelocity Engine, Run Length Encoding, Auto Sort. 2. You Can Get Kicked Out of Your Own Party. 3. Segmentation. Compression. Memory Consumption.
- 13. TABLEColor Value Red 10 Blue 15 Green 13 Green 25 Yellow 31 Yellow 41 Red 11 Blue 14 Purple 21 Orange 6 Red 15 Follow Along: Colors
- 14. DICTIONARYID Color Value 0 Red 10 1 Blue 15 2 Green 13 2 Green 25 3 Yellow 31 3 Yellow 41 0 Red 11 1 Blue 14 4 Purple 21 5 Orange 6 0 Red 15 Dictionary ID Color 0 Red 1 Blue 2 Green 3 Yellow 4 Purple 5 Orange
- 15. DICTIONARYID Color Value 0 Red 10 1 Blue 15 2 Green 13 2 Green 25 3 Yellow 31 3 Yellow 41 0 Red 11 1 Blue 14 4 Purple 21 5 Orange 6 0 Red 15 Dictionary ID Color 0 Red 1 Blue 2 Green 3 Yellow 4 Purple 5 Orange
- 16. COLUMN ENCODING ID Value 0 10 1 15 2 13 2 25 3 31 3 41 0 11 1 14 4 21 5 6 0 15 Dictionary ID Color 0 Red 1 Blue 2 Green 3 Yellow 4 Purple 5 Orange
- 17. EVALUATION ID Value 0 10 1 15 2 13 2 25 3 31 3 41 0 11 1 14 4 21 5 6 0 15 Dictionary ID Color Value (SUM) 0 Red 36 1 Blue 29 2 Green 38 3 Yellow 72 4 Purple 21 5 Orange 6
- 18. OPTIMAL SORT ORDER ID Value 0 10 0 11 0 15 1 15 1 14 2 13 2 25 3 31 3 41 4 21 5 6 Dictionary ID Color 0 Red 1 Blue 2 Green 3 Yellow 4 Purple 5 Orange
- 19. RUN LENGTH ENCODING ID Value 0 10 0 11 0 15 1 15 1 14 2 13 2 25 3 31 3 41 4 21 5 6 Dictionary ID Color Start Count 0 Red 1 3 1 Blue 4 2 2 Green 6 2 3 Yellow 8 2 4 Purple 10 1 5 Orange 11 1 Dictionary ID Color 0 Red 1 Blue 2 Green 3 Yellow 4 Purple 5 Orange
- 20. ID Value 0 10 0 11 0 15 1 15 1 14 2 13 2 25 3 31 3 41 4 21 5 6 EVALUATION Dictionary ID Color Value (SUM) 0 Red 36 1 Blue 29 2 Green 38 3 Yellow 72 4 Purple 21 5 Orange 6
- 21. BEHIND THE CURTAIN 1. xVelocity Engine, Run Length Encoding, Auto Sort. 2. You Can Get Kicked Out of Your Own Party. 3. Segmentation. Compression. Memory Consumption.
- 22. CARDINALITY 1. High-cardinality: columns with values that are very uncommon or unique. • DateTime • Unique IDs (Not Needed For Relationships) • Text (Freeform Fields) • Numbers (Floating Point Precision) 2. Low-cardinality: columns with relatively few unique values. • Date (365 Days a Year) • Time (86,400 Seconds) • True / False • Currency (4 Digits after Decimal)
- 23. BEHIND THE CURTAIN 1. xVelocity Engine, Run Length Encoding, Auto Sort. 2. You Can Get Kicked Out of Your Own Party. 3. Segmentation. Compression.
- 24. RESOURCE CONSUMPTION 1M 1M 1M … Model Compressed Segment Compressed Segment Data Model (Hierarchies, Calculated Columns, Relationships) Compressed Segment Compressed Segment Compressed Segment … CPU MEMORY CPU
- 25. SEGMENTATION In the above Vertipaq Analyzer we review a DateTime field with high-cardinality. The Rows count is equal to the Cardinality count and is loaded in an uncompressed state to the xVelocity Engine. At the rate of 1M rows per segment, 9 uncompressed segments are loaded as a single partition into memory. Full Solution: DateTime Series Analyzer
- 26. SEGMENTATION In the above Vertipaq Analyzer we review a Time and Date field with low-cardinality. The Order Time field contains 86,400 unique values (equal to the number of seconds in a day) and the Order Date 103 unique values. At the rate of 1M rows per segment, 85 compressed segments are loaded as a single partition into memory. Full Solution: Date and Time Series Analyzer
- 28. TAKEAWAYS 1. Only bring in what you need. 2. DateTime = BAD; unless necessary. 3. Calculated Column = POST compression. • Perform Calculations Close to the Source (SQL or within Power Query)
- 29. SWITCH ( TRUE(), YOUR , BATTLES )
- 30. END USE 1. Be cautious with preview features. 2. Don’t assume your audience can’t learn. 3. Consistency. Consistency. Consistency. 4. Don’t risk your brand over someone else’s need for a pie chart. 5. Champion modern analytics within your organization. • By 2020, 50% of analytical queries either will be generated via search, natural language processing or voice, or will be automatically generated. (Gartner)
- 31. CLOSING REMARKS DELIVER BETTER THAN THE ASK.
- 32. QUESTIONS? SUGGESTIONS? YOU THINK I’M CRAZY?!
- 33. Connect Online @notaboutthecell /in/alexmpowers It’s Not About The Cell