This document provides an overview of advanced dimensional modelling techniques. It discusses: 1) Dimension structures such as slowly changing dimension type 6, using one or two dimensions, and when to snowflake dimensions. 2) Fact table considerations like primary keys, snapshotting transaction fact tables, aggregate fact tables, and vertical fact tables. 3) Dimension behaviors like rapidly changing dimensions, very large dimensions, banding and stamping dimension rows, and dimensions with multi-valued attributes. 4) Combination techniques involving real-time fact tables, dealing with currency rates and status values. The document covers several sections and many modelling patterns in 44 slides.

1. Advanced Dimensional Modelling SQLBits 8, 9th April 2011, Brighton Vincent Rainardi vrainardi@gmail.com Blog: dwbi1.wordpress.com

3. 1 or 2 Dimensions

4. When To Snowflake

5. A Dimension with Only 1 Attribute

7. Snapshotting Transaction Fact Tables

8. Aggregate Fact Tables

10. Very Large Dimensions

11. Banding Dimension Rows

12. Stamping Dimension Rows

14. Dealing with Currency Rates

15. Dealing with Status

16. 4 sections: 2 dims, 1 fact, 1 combi. Lots of material, may not able to finish.

17. 44 slides, some slides we may have to touch lightly.

19. SCD Type 6 2/2 Used for “As Was” reporting e.g. balances by tariff (price plan) at the end of last year,if the customers were on today’s tariff. Fact “Type 12” Dim Natural Key

21. Disadvantage: can’t go from account to customer without going through the fact table - performance

22. Simplicity, 1 dim

23. Hierarchy from customer attribute &account attribute

25. Modular: 2 separate dim tables but we can combine them easily to create a bigger dimension

26. To get the breakdown of a measure by a customer attribute is a bit more complicated than a)select c. attribute, sum(f.measure1) from fact1 finner join dim_account a on f.account_key = a.account_keyinner join dim_customer c on a.customer_key = c.customer_keygroup by c. attribute

28. We can access dim customer directly from the fact table.Fact Table DimAccount DimCustomer Weakness: maintain customer key in 2 places: fact table and dim account. a.k.a. “Star with a Back Door”

29. 1 or 2 dimensions 4/4 e) One Dimension with Customer Key Fact Table Fact Table Try to fix weakness of a: unable to build a fact table with grain = customer. Add a column in dim account: customer key DimAccount Not as popular as c) and d) in solving Dim Customer issue. It is “indecisive” : trying to create Dim Customer but doesn’t want to create Dim Customer. Disadvantage: Dim Customer is hidden inside Dim Account, making it: a) more difficult to maintain (especially for a type 2), and b) less modular/flexible

30. When to Snowflake 1/3 1. When the sub dim is used by several dims City-Country-Region columns exist in DimBroker, DimPolicy, DimOffice and DimInsured Replaced by Location/GeoKey pointing to DimLocation / DimGeography Advantage: consistent hierarchy, i.e. relationship between City, Country & Region. Weakness: we would lose flexibility. City to Country are more or less fixed, but the grouping of countries might be different between dimensions.

32. DimManufacturer is used in DimProduct, and is also used in the fact table.

33. DimProductGroup is used in DimProduct, and is also used in some fact table.The alternative is maintaining two full dimensions (star classic).

34. When to Snowflake 3/3 3. To make “base dim” and “detail dim” Insurance classes, account types (banking), product lines, diagnosis, treatment (health care) Policies for marine, aviation & property classes have different attributes. Pull common attributes into 1 dim: DimBasePolicy Put class-specific attributes into DimMarine, DimProperty, DimAviation Ref: Kimball DW Toolkit 2nd edition page 213 4. To enrich a date attribute Month, Quarter, Year, etc. Like #1, a sub dim used by several dims.

35. A dimension with only 1 attribute 1/2 Should we put the attribute in the fact table? (like DD = Degenerate Dim) Probably, if the grain = fact table,and it’s short or it’s a number. Reasons for putting single attribute in its own dim: Keep fact table slim (4 bytes int not 100 bytes varchar) When the value changes, we don’t have to update the BIGfact table – ETL performance Grain is much lower than fact table – small dim Yes it’s only 1 attribute today, but in the future there could be another attribute. Could become a junk dim.

36. A dimension with only 1 attribute 2/2 Exception: snapshot month (or day/week/quarter) Snapshot month is used in periodic snapshot fact table. Snapshot month is in the form of an integer (201104 for April 2011). Doesn’t violate the 3 points above. It is an integer, not char(6). The value never changes, April 2011 will be April 2011 forever There will not be other attributes in the dim

37. Transaction Level Dimension 1/5 A dim with grain = the transaction fact table Transaction, not accumulative or periodic snapshot Examples: IT Helpdesk DW: Dim Ticket Telco DW: Dim Call Banking/Asset MgtDW: Dim Trade Insurance DW: Dim Premium TransactionLevel Dim Most granular event in any business process

38. Transaction Level Dimension 2/5 Advantages: Query PerformanceDD columns are moved to a dim, away from the heavy traffic in fact tables. DW queries don’t touch those DD columns unless they need to– performance. DD attributes totalling 30 bytes, replaced by 4 bytes int column. Slimmer fact table, better for queries. Periodic Snapshot Fact TableFor periodic snapshot fact table, saving is even greater. Monthly snapshot fact, 10 years / 120 months. Rather than specifying the DDs repeatedly 120x, they are specified once in the transaction dim. All that is left on the fact table is a slim 1 intcol: the transaction key.

39. Transaction Level Dimension 3/5 Some fact tables have grains greater than the transactionA payment from a customer is posted into 4 accounts in the GL fact table. That single financial transaction becomes 4 fact rows but only has 1 row in the trans dim. Fact table with 10m rows, trans dim only 3 million rows. Related TransactionsSome transactions are related, e.g. in retail, a purchase of a kitchen might need to be created as 2 related orders, because the worktop is made-to-order. Rather than creating a ‘related order’ column on the fact tables, it might be better (depends on how it’s used) to create it on the trans dim because: a) an order can consist of many fact rows (1 row per item) so the “related order number” will be duplicated across these fact rowsb) slimmer fact tablec) the transaction could be on many fact tables, not only one.

41. Mart/DW only used for SSAS: there is little point of having trans dim physically. In SSAS we can create the transaction dimension “on the fly” from the fact table (“fact dimension”).

42. Using trans dim to put attributes as opposed to put them in the main dimensions, with the argument of: that’s the value of the attribute when the transaction happened – this is not right, use type 2 SCD for this.Main Acct type Trans Location

45. 1 or 2 Dimensions

46. When To Snowflake

47. A Dimension with Only 1 Attribute

49. Snapshotting Transaction Fact Tables

50. Aggregate Fact Tables

52. Very Large Dimensions

53. Banding Dimension Rows

54. Stamping Dimension Rows

56. Dealing with Currency Rates

57. Dealing with Status25% Time, Questions

58. Fact Table Primary Key 1/3 Should we have a PK? Yes, if we need to be able to identify each fact row Need to refer to a fact row from another fact row e.g. chain of events Many identical fact rows and we need to update/delete only one To link the fact table to another fact table Some experts totally disagree Uniqueness Related Trans Header - Detail PK PK FK PK FK (no RI) (not enforced) previous/next transaction

59. Fact Table Primary Key 2/3 Single or Multi Column? Single Column: Generated Identity Multi Column: Dimension Keys Single-column PK is better than multi-column PK because : 1) A multi-column PK may not be unique. A single-column PK guarantees that the PK is unique, because it is an identity column. 2) A single-column PK is slimmer than a multi-column PK, better query performance. To do a self join in the fact table (e.g. to link the current fact row to the previous fact row), we join on a single integer column.

60. Fact Table Primary Key 3/3 Advantage: Prevent duplicate rows, query performance Disadvantage: loading performance Indexing the PK: cluster or not? Cluster the PK if: the PK is an identity column Don’t cluster the PK if: the PK is a composite, or when you need the cluster index for query performance (with partitioning) Example of not having a PK If duplicate fact rows are allowed. e.g. retail DW: Store Key, Date Key, Product Key, Customer Key Same customer buying the same milk in the same shop on the same day twice --- Order Line ID as DD to make it unique (not all EPOS has it)

61. Snapshotting Transaction Fact Tables 1/1 Potentially huge – billions rows Only take what you need Smart date key/month, e.g. 20110409 Monthly or daily Trunc-reload of current month/day Daily (4 wk), Weekly (1 yr), Monthly (10 yr) Purging & Archiving Load from staging (cached) Index/partition on snapshot date Trans Staging Snapshot

62. Aggregate Fact Tables 1/2 What are they? High level aggregation of base fact tables A “select group by” query on a 2 billion rows fact table can take 30 mins if it joins with two big fact tables, even with indexes in place So we do this query in advance as part of the DW load and store it as an Aggregate Fact Table The report only takes 1 second to run. Base Fact Tables 30 mins Aggregate Fact Table 1 sec Report

63. Aggregate Fact Tables 2/2 What For? For report performance (group by is costly) BO: aggregate aware Not SSAS: aggregate in cubes, not tables Loading & indexing: Best to load from staging (at the same time as loading the main fact table) not from the main fact table (this would be working 2x) Partition for data distribution or narrow query Indexing: by the main dim keys

64. Vertical Fact Tables 1/1 Normalised 1 measure column The meaning of that measure column depends on “measure type” column Used for Finance/GL mart Advantage: flexibility: using accounts, balance, Dr Cr Disadvantage: non additive “Normal” Fact Table many measures (actual & budget) Measure Type Dim Key Vertical Fact Table 1 measure

66. 1 or 2 Dimensions

67. When To Snowflake

68. A Dimension with Only 1 Attribute

70. Snapshotting Transaction Fact Tables

71. Aggregate Fact Tables

73. Very Large Dimensions

74. Banding Dimension Rows

75. Stamping Dimension Rows

77. Dealing with Currency Rates

78. Dealing with Status50% Time, Questions

79. Rapidly Changing Dimension 1/1 Why is it a problem Large SCD2 dim – Attributes change every day Slow query when join with large fact tables What to do Put into a separate dim, link direct to fact table. Just store the latest, type 1 attributes (or dual) Store in the fact table (for small attribute, e.g. indicator) Type2 Type2 Type2 Type2 Type1

80. Very Large Dimension Why is it a problem SSAS: 4 GB string store limit for dimension SSAS: dim is “select distinct” on each attribute – long processing time “Valid date” join on SCD2 for as was Usually customer dim where the “quality stamp” changes daily or because of high number of distinct values Difficult to browse high cardinality attribute Join with fact tables – performance 1/2

81. Very Large Dimension 2/2 What to do Split into 2 dims, same grain. Always cut vertically. Remove SCD2, or at least only certain columns. Most common: separate the attributes with high cardinality/change frequency Bucketing/banding, group values into ranges VLD

82. Banding Dimension Rows 1/1 It is grouping numerical values (numerical attributes, not measure) into several bands, e.g. engine size, distance from station, amount purchased (last complete year). Benefits: easier for analysis & reporting, comparing between categories. Issue/problem: limit e.g. bucketing criteria1 hour to implement, 3 months to argue

84. To reflect c0nsumer interest on the product (product categorisation based on customer interest level)

85. Any other dates or measures summarized as stamped attribute, i.e. “new customer”, “big spender”, or results from recommendation analysis/algorithm e.g. customer behaviour based on previous purchases.

86. Used for analysis / reporting“Stamped” Attributes

89. Dimensions with Multi Valued Attributes 3/4 3. Use a bridge table to link the 2 dims Fact Table Dim Size Bridge Table Dim Product 4. Have several columns in the dim for that attribute If the number of attributes is small and fixed, this is a popular approach. But if the number of attributes is large (e.g. >10) or if it’s variable (e.g. sometimes 2, sometimes 20), approach 2 and 3 above are more popular, and more appropriate.

90. Dimensions with Multi Valued Attributes 4/4 5. Put the attribute in a snowflake sub dim We can’t really do this, as it is 1 to many (1 row in the main dim corresponds to many rows in the sub dim). So we need a bridge table, which brings us back to approach 3. 6. Keep in one column using delimiters e.g. “Small|Medium″. A crazy idea. More flexible than having several columns (approach 4) and simpler than approach 3 or 2. If the purpose of the attribute is “display only” on a report (rather than analyse or slice & dice), there is an argument for using this approach, particularly if the number of attributes is small (e.g. 1 to 4).

92. 1 or 2 Dimensions

93. When To Snowflake

94. A Dimension with Only 1 Attribute

96. Snapshotting Transaction Fact Tables

97. Aggregate Fact Tables

99. Very Large Dimensions

100. Banding Dimension Rows

101. Stamping Dimension Rows

103. Dealing with Currency Rates

104. Dealing with Status75% Time, Questions

105. Real Time Fact Table 1/1 Reporting the transaction system in real time View to union with the normal fact table, or use partitions Freezing the dims for key lookup, -3 unknown key Key corrections next day Dims as of yesterday Main partition (up to last night) Unknown keys: -1 null in source -2 not in dim table -3 not in dim table as dim was frozen to be resolved next batch Real time partition (intraday today) dimkey

106. Dealing with Currency Rates 1/3 What for/background/requirements Report in 3 reporting currencies, using today rates or past Analyse over time without the impact of currency rates (using fixed currency rates, e.g. 2010 EOY rates) Had the transactions happened today Currency rates historical analysis Reporting Currency DW Currency Transaction Currency Transaction Rates Reporting Rates 100 countries 40 currencies 3-4 currencies GBP, USD, EUR, Original 1 currency (many transaction dates) ( 1 reporting date) e.g. GBP

107. Dealing with Currency Rates 2/3 Approaches Store in original currencies, convert to DW currency at runtime.Or convert at load, store in DW currency – inaccuracy. Or store in both original and DW currency Currency rate fact table (date, currency, rate)Or store rates in the fact table On report/cube: date input at run time (default = today) Fact Tables FX Fact Table Rate

108. Dealing with Currency Rates 3/3 in original currency, DW currency or both Concept of FX Rate Type/Profile

109. Dealing with Status 1/2 What/background Workflow (policies, contracts, documents) Bottleneck analysis (no of days between stages) How many on each stage Status 1 Status 4 Status 6 Status 2 date2 date3 date4 date1 Status 3 Status 5

110. Dealing with Status 2/2 Approaches Accumulative Snapshot Fact, 1 row per application SCD2 on DimApp App Status fact table

111. Thanks Email: vrainardi@gmail.com Blog: http://dwbi1.wordpress.comCovers many of the topics in this presentation This PowerPoint: in my blog, scroll to bottom, click on “SQLBit8” Special thanks to Guang Ming Xing and Simon Jensen who helped reviewing this presentation and provided useful comments (doesn’t mean that they agree with the content)