1. Introduction to tabular models
Welcome to Tabular
Introduction to tabular models
Dustin Ryan
@SQLDusty

2. Who’s this guy?
Dustin Ryan – Senior B.I. Consultant & Trainer at Pragmatic Works
Data warehouse design, SSRS, SSIS, SSAS, SharePoint BI, Power BI
Author, contributor, technical editor
PASS Summit, SQL Rally, SQL Saturday, Code Camp
My family, sleep, preparing for the war against the machines

3. Agenda Making the right choice Building a Tabular Model Best Practices
Resources

4. SSAS showdown
Cubes
Tabular model

5. Yes (detail opens in separate worksheet)
Feature parity
Multidimensional
Tabular
Power Pivot
Actions
Yes No
Aggregations
Calculated Measures
Custom Assemblies
Custom Rollups
Distinct Count
Yes (via DAX)
Drill through
Yes (detail opens in separate worksheet)
Hierarchies
KPIs
Linked objects
Yes (linked tables)
Many-to-many relationships
Parent-child Hierarchies
Partitions
Perspectives
Semi-additive Measures
Translations
User-defined Hierarchies
Writeback

6. Considerations Scalability Performance Time to develop
Complex business problems
Learning curve

7. Scalability Tabular Multidimensional In-Memory Technology (x-Velocity)
Can Store Large Amounts of Data
No Aggregations. Column-Based Storage.
Data Compression 10x
Pre-Aggregated Data From Disk
Can Store Very Large Amounts of Data
Uses Aggregations to Increase Query Performance
Data Compression 3x
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8. Performance Tabular Multidimensional
Generally Speaking Tabular will perform faster
Tabular Engine Does Not Require a Great Deal of Performance Tuning
Best at Returning Low Granularity Data
Need memory optimized hardware
Pre-Aggregated Data From Disk
Can Store Very Large Amounts of Data
Uses Aggregations to Increase Query Performance
Often Faster Than Tabular When Pulling From Warm Cache
Need storage optimized hardware
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9. Time to Develop Tabular Multidimensional By Far Less Time to Develop
Can Upgrade From Power Pivot
Does Not Require Dimensional Model (But it’s still a good idea)
Much Simpler Interface for Creating Model
Long Planning and Development Cycles
No Upgrade Path
Requires Dimensional Model
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10. Complex Business problems
Tabular
Multidimensional
Can Handle Complex Relationships with DAX
Has Built-in Capabilities for Complex Relationships
Role Playing
Parent-Child
Many-to-many
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11. Learning Curve Tabular Multidimensional
Uses DAX (Data Analysis Expressions) for Query Language
If You Know Excel Formulas Then DAX Will Be Easy
Simple Drag and Drop KPI Creation
Relationships are simple (no composite keys)
Uses MDX (Multi-Dimensional Expressions) for Query Language
Difficult to Learn but has Benefits (Navigating Hierarchies)
More Complex KPI Creation
Multiple relationship types
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12. Other considerations After you Choose…
There is No Migration Path to the Other Technology
Tabular Best on its Own Machine, Not a Good Candidate for a Shared Service due to differences in hardware requirements
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13. The Bottom line Consider Tabular… Consider Multidimensional…
If You Have a Short Development Timeline
If You are Working with a Plethora of Memory
If You Data Model is Simple
If You Have Many Disparate Data Sources
If Users Need to Query Large Amounts of Detail Data
Consider Multidimensional…
If You are Using SQL Server 2008 R2 or Earlier
If You Have a Multi-Terabyte Data Source
If You Have a Complex Data Model
If You Need Multidimensional only features (Actions, Data Mining, Writeback, Translations)
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14. To the Tabular Model!
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15. Resources
SQL Server 2012 Analysis Services The BISM Tabular Model (link) Practical PowerPivot & DAX Formulas for Excel 2010 (link) Performance Tuning of Tabular Models in SQL Server 2012 Analysis Services Whitepaper (link)
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16. Thanks for attending my session!
Contact Me
@SQLDusty (link) (link)
Thanks for attending my session!
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