Dimensional modeling primer - SQL Saturday Madison - April 11th, 2015
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This document provides a summary of a presentation on dimensional data modeling. It begins with introducing the presenter and their background. It then covers key concepts in dimensional modeling including facts, dimensions, and different modeling approaches like star schemas and snowflakes. It discusses more complex concepts like multi-valued dimensions, slowly changing dimensions, and hierarchies. It concludes by discussing why dimensional modeling is used and provides tips on how to start dimensional modeling a database.
Dimensional modeling primer - SQL Saturday Madison - April 11th, 2015
- 1. Dimensional Data Modeling A Primer SQL Saturday Madison – April 11,2015
- 3. Who Am I? • Terry Bunio • Data Base Administrator – Oracle – SQL Server 6,6.5,7,2000,2005,2008,2012 – Informix – ADABAS • Data Modeler/Architect – Investors Group, LPL Financial, Manitoba Blue Cross, Assante Financial, CI Funds, Mackenzie Financial – Normalized and Dimensional • Agilist – Innovation Gamer, Team Member, SQL Developer, Test writer, Sticky Sticker, Project Manager, PMO on SAP Implementation
- 4. Previous SQL Saturday Presentations • SQL Sat Winnipeg 2014 – Breaking Data – Stress test using Ostress • SQL Sat Madison/Minneapolis 2014 – A data driven ETL test framework • SQL Sat Minneapolis 2013 – Agile Data Warehouse • SQL Sat Fargo 2013 – SSRS and SharePoint – there and back again
- 13. Agenda • Data Modeling – #1 mistake In Data Modeling • Database Design Methods • Dimensional concepts – Facts – Dimensions • Complex Concept Introduction • Why and How? • My Top 10 Dimensional Modeling Recommendations
- 14. Definition • “A database model is a specification describing how a database is structured and used” – Wikipedia
- 15. Definition • “A data model describes how the data entities are related to each other in the real world” – Terry (5 years ago) • “A data model describes how the data entities are related to each other in the application” – Terry (today)
- 16. Data Model Characteristics • Organize/Structure like Data Elements • Define relationships between Data Entities • Highly Cohesive • Loosely Coupled
- 17. Anthropomorphism
- 18. #1 Mistake in Data Modeling • Modeling something to take on human characteristics or characteristics of our world
- 19. Amazon
- 20. Amazon • Warehouse is organized totally randomly • Although humans think the items should be ordered in some way, it does not help storage or retrieval in any way – In fact in hurts it by creating ‘hot spots’ for in demand items
- 21. Data Model Anthropomorphism • We sometimes create objects in our Data Models are they exist in the real world, not in the applications
- 22. Data Model Anthropomorphism • This is usually the case for physical objects in the real world – Companies/Organizations – People – Addresses – Phone Numbers – Emails
- 23. Data Model Anthropomorphism • Why? – Do we ever need to consolidate all people, addresses, or emails? • Rarely – We usually report based on other filter criteria – So why do we try to place like real world items on one table when applications treat them differently?
- 24. Over Engineering
- 25. Over Engineering • Additional flexibility that is not required does not simplify the solution, it overly complicates the solution
- 27. Two design methods • Relational – Database normalization is the process of organizing the fields and tables of a relational database to minimize redundancy and dependency. Normalization usually involves dividing large tables into smaller (and less redundant) tables and defining relationships between them. The objective is to isolate data so that additions, deletions, and modifications of a field can be made in just one table and then propagated through the rest of the database via the defined relationships.”
- 28. Two design methods • Dimensional – Dimensional modeling always uses the concepts of facts (measures), and dimensions (context). Facts are typically (but not always) numeric values that can be aggregated, and dimensions are groups of hierarchies and descriptors that define the facts
- 29. Relational
- 30. Relational • Relational Analysis – Database design is usually in Third Normal Form – Database is optimized for transaction processing. (OLTP) – Normalized tables are optimized for modification rather than retrieval
- 31. Normal forms • 1st - Under first normal form, all occurrences of a record type must contain the same number of fields. • 2nd - Second normal form is violated when a non-key field is a fact about a subset of a key. It is only relevant when the key is composite • 3rd - Third normal form is violated when a non-key field is a fact about another non-key field Source: William Kent - 1982
- 32. Dimensional
- 33. Dimensional • Dimensional Analysis – Star Schema/Snowflake – Database is optimized for analytical processing. (OLAP) – Facts and Dimensions optimized for retrieval • Facts – Business events – Transactions • Dimensions – context for Transactions – People – Accounts – Products – Date
- 34. Relational • 3 Dimensions • Spatial Model – No historical components except for transactional tables • Relational – Models the one truth of the data – One account ‘11’ – One person ‘Terry Bunio’ – One transaction of ‘$100.00’ on April 10th
- 35. Dimensional • 4 Dimensions • Temporal Model – All tables have a time component • Dimensional – Models the data over time – Multiple versions of Accounts over time – Multiple versions of people over time – One transaction • Transactions are already temporal
- 38. Kimball-lytes • Bottom-up - incremental – Operational systems feed the Data Warehouse – Data Warehouse is a corporate dimensional model that Data Marts are sourced from – Data Warehouse is the consolidation of Data Marts – Sometimes the Data Warehouse is generated from Subject area Data Marts
- 39. Inmon-ians • Top-down – Corporate Information Factory – Operational systems feed the Data Warehouse – Enterprise Data Warehouse is a corporate relational model that Data Marts are sourced from – Enterprise Data Warehouse is the source of Data Marts
- 40. The gist… • Kimball’s approach is easier to implement as you are dealing with separate subject areas, but can be a nightmare to integrate • Inmon’s approach has more upfront effort to avoid these consistency problems, but takes longer to implement.
- 41. Facts
- 42. Fact Tables • Contains the measurements or facts about a business process • Are thin and deep • Usually is: – Business transaction – Business Event • The grain of a Fact table is the level of the data recorded.
- 43. Fact Tables • Contains the following elements – Primary Key - Surrogate – Timestamp – Measure or Metrics • Transaction Amounts – Foreign Keys to Dimensions – Degenerate Dimensions • Transaction indicators or Flags
- 44. Fact Tables • Types of Measures – Additive - Measures that can be added across any dimensions. • Amounts – Non Additive - Measures that cannot be added across any dimension. • Rates – Semi Additive - Measures that can be added across some dimensions. • Don’t have a good example
- 45. Fact Tables • Types of Fact tables – Transactional - A transactional table is the most basic and fundamental. The grain associated with a transactional fact table is usually specified as "one row per line in a transaction“. – Periodic snapshots - The periodic snapshot, as the name implies, takes a "picture of the moment", where the moment could be any defined period of time. – Accumulating snapshots - This type of fact table is used to show the activity of a process that has a well-defined beginning and end, e.g., the processing of an order. An order moves through specific steps until it is fully processed. As steps towards fulfilling the order are completed, the associated row in the fact table is updated.
- 46. Special Fact Tables • Degenerate Dimensions – Degenerate Dimensions are Dimensions that can typically provide additional context about a Fact • For example, flags that describe a transaction • Degenerate Dimensions can either be a separate Dimension table or be collapsed onto the Fact table – My preference is the latter
- 47. Special Fact Tables • If Degenerate Dimensions are not collapsed on a Fact table, they are called Junk Dimensions and remain a Dimension table • Junk Dimensions can also have attributes from different dimensions – Not recommended
- 48. Dimensions
- 49. Dimension Tables • Unlike fact tables, dimension tables contain descriptive attributes that are typically textual fields • These attributes are designed to serve two critical purposes: – query constraining and/or filtering – query result set labeling. Source: Wikipedia
- 50. Dimension Tables • Shallow and Wide • Usually corresponds to entities that the business interacts with – People – Locations – Products – Accounts
- 51. Time Dimension
- 52. Time Dimension • All Dimensional Models need a time component • This is either a: – Separate Time Dimension (recommended) – Time attributes on each Fact Table
- 53. Dimension Tables • Contains the following elements – Primary Key – Surrogate – Business Natural Key • Person ID – Effective and Expiry Dates – Descriptive Attributes • Includes de-normalized reference tables
- 54. Behavioural Dimensions • A Dimension that is computed based on Facts is termed a behavioural dimension
- 55. Mini-Dimensions
- 56. Mini-Dimensions • Splitting a Dimension up due to the activity of change for a set of attributes • Helps to reduce the growth of the Dimension table
- 57. Slowly Changing Dimensions • Type 1 – Overwrite the row with the new values and update the effective date – Pre-existing Facts now refer to the updated Dimension – May cause inconsistent reports
- 58. Slowly Changing Dimensions • Type 2 – Insert a new Dimension row with the new data and new effective date – Update the expiry date on the prior row • Don’t update old Facts that refer to the old row – Only new Facts will refer to this new Dimension row • Type 2 Slowly Changing Dimension maintains the historical context of the data
- 59. Slowly Changing Dimensions • A type 2 change results in multiple dimension rows for a given natural key • A type 2 change results in multiple dimension rows for a given natural key • A type 2 change results in multiple dimension rows for a given natural key
- 60. Slowly Changing Dimensions • No longer to I have one row to represent: – Account 10123 – Terry Bunio – Sales Representative 11092 • This changes the mindset and query syntax to retrieve data
- 61. Slowly Changing Dimensions • Type 3 – The Dimension stores multiple versions for the attribute in question • This usually involves a current and previous value for the attribute • When a change occurs, no rows are added but both the current and previous attributes are updated • Like Type 1, Type 3 does not retain full historical context
- 62. Slowly Changing Dimensions • You can also create hybrid versions of Type 1, Type 2, and Type 3 based on your business requirements
- 63. Type 1/Type 2 Hybrid • Most common hybrid • Used when you need history AND the current name for some types of statutory reporting
- 64. Frozen Attributes • Some times it is required to freeze some attributes so that they are not Type 1, Type 2, or Type 3 • Usually for audit or regulatory requirements
- 65. Conformity
- 66. Recall - Kimball-lytes • Bottom-up - incremental – Operational systems feed the Data Warehouse – Data Warehouse is a corporate dimensional model that Data Marts are sourced from – Data Warehouse is the consolidation of Data Marts – Sometimes the Data Warehouse is generated from Subject area Data Marts
- 67. The problem • Kimball’s approach can led to Dimensions that are not conforming • This is due to the fact that separate departments define what a client or product is – Some times their definitions do not agree
- 68. Conforming Dimension • A Dimension is said to be conforming if: – A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts. A conformed dimension cuts across many facts. • Dimensions are conformed when they are either exactly the same (including keys) or one is a perfect subset of the other.
- 69. If you take one thing away • Ensure that your Dimensions are conformed
- 70. Complexity
- 71. Complexity • Most textbooks stop here only show the simplest Dimensional Models • Unfortunately, I’ve never run into a Dimensional Model like that
- 72. Simple
- 73. More Complex
- 74. Real World
- 75. Complex Concept Introduction • Snowflake vs Star Schema • Multi-Valued Dimensions and Bridges • Multi-Valued Attributes • Factless Facts • Recursive Hierarchies
- 78. Snowflake vs Star Schema • These extra table are termed outriggers • They are used to address real world complexities with the data – Excessive row length – Repeating groups of data within the Dimension • I will use outriggers in a limited way for repeating data
- 79. Multi-Valued Dimensions • Multi-Valued Dimensions are when a Fact needs to connect more than once to a Dimension – Primary Sales Representative – Secondary Sales Representative
- 80. Multi-Valued Dimensions • Two possible solutions – Create copies of the Dimensions for each role – Create a Bridge table to resolve the many to many relationship
- 82. Bridge Tables
- 83. Bridge Tables • Bridge Tables can be used to resolve any many to many relationships • This is frequently required with more complex data areas • These bridge tables need to be considered a Dimension and they need to use the same Slowly Changing Dimension Design as the base Dimension – My Recommendation
- 84. Multi-Valued Attributes • In some cases, you will need to keep multiple values for an attribute or sets of attributes • Three solutions – Outriggers or Snowflake (1:M) – Bridge Table (M:M) – Repeat attributes on the Dimension • Simplest solution but can be hard to query and causes long record length
- 85. Factless Facts • Fact table with no metrics or measures • Used for two purposes: – Records the occurrence of activities. Although no facts are stored explicitly, these events can be counted, producing meaningful process measurements. – Records significant information that is not part of a business activity. Examples of conditions include eligibility of people for programs and the assignment of Sales Representatives to Clients
- 87. Hierarchies and Recursive Hierarchies • We would need a separate session to cover this topic • Solution involves defining Dimension tables to record the Hierarchy with a special solution to address the Slowly Changing Dimension Hierarchy • Any change in the Hierarchy can result in needing to duplicate the Hierarchy downstream
- 88. Why? • Why Dimensional Model? • Allows for a concise representation of data for reporting. This is especially important for Self-Service Reporting – We reduced from 400+ tables in our Operational Data Store to 60+ tables in our Data Warehouse – Aligns with real world business concepts
- 89. Why? • The most important reason – – Requires detailed and deeper understanding of the data – Validates the solution – Uncovers inconsistencies and errors in the Normalized Model • Easy for inconsistencies and errors to hide in 400+ tables • No place to hide when those tables are reduced down
- 90. Why? • Ultimately there must be a business requirement for a temporal data model and not just a spatial one. • Although you could go through the exercise to validate your understanding and not implement the Dimensional Data Model
- 91. How?
- 92. How? • Start with your simplest Dimension and Fact tables and define the Natural Keys for them – i.e. People, Product, Transaction, Time • De-Normalize Reference tables to Dimensions (And possibly Facts based on how large the Fact tables will be) – I place both codes and descriptions on the Dimension and Fact tables • Look to De-normalize other tables with the same Cardinality into one Dimension – Validate the Natural Keys still define one row
- 93. How? • Don’t force entities on the same Dimension – Tempting but you will find it doesn’t represent the data and will cause issues for loading or retrieval – Bridge table or mini-snowflakes are not bad • I don’t like a deep snowflake, but shallow snowflakes can be appropriate • Don’t fall into the Star- Schema/Snowflake Holy War – Let your data define the solution
- 94. How? • Iterate, Iterate, Iterate – Your initial solution will be wrong – Create it and start to define the load process and reports – You will learn more by using the data than months of analysis to try and get the model right
- 95. Top 10
- 96. Top 10 1. Copy the design for the Time Dimension from the Web. Lots of good solutions with scripts to prepopulate the dimension 2. Make all your attributes Not-Null. This makes Self-Service Report writing easy 3. Create a single Surrogate Primary Key for Dimensions – This will help to simplify the design and table width – These FKs get created on Fact tables !
- 97. Top 10 4. Never reject a record – Create an Dummy Invalid record on Each Dimension. Allows you to store a Fact record when the relationship is missing 5. Choose a Type 2 Slowly Changing Dimension as your default 6. Use Effective and Expiry dates on your Dimensions to allow for maximum historical information – If they are Type 2!
- 98. Top 10 7. SSIS 2012 has some built-in functionality for processing Slowly Changing Dimensions – Check it out! 8. Add “Current_ind” and “Dummy_ind” attributes to each Dimension to assist in Report writing 9. Iterate, Iterate, Iterate 10. Read this book
- 101. Whew! Questions?