1. SQL Server Analysis Services Tabular Model
A brief end to end
By Kirsten burnett - ENTELECT

2. Our Journey through Tabular Models
Tabular in a nut shell
The Business Intelligence Semantic Model
A brief history of Tabular
Tabular benefits over Power Pivot
Tabular vs Multidimensional and choosing between the two
xVelocity In Memory Analytics Engine
DirectQuery
Managing a Tabular Model (Processing and Partitioning)
Automating with ASSL/TMSL and AMO
Creating and Deploying a Tabular model (Demo)
Tabular and xVelocity in the market and the future for SQL Server Analysis Services
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3. Tabular in a nutshell Tabular SSAS Database
Measures, KPI’s, Hierarchies
Perspectives & Translations
DAX
Partitioning & Processing
DirectQuery
ASSL, AMO, TMSL, DMV’s
xVelocity
Role and Row Security
Tabular
Relationships, Tables, Columns, Rows
Various Data Sources
SSAS Database
SSDT & SSMS

4. Business Intelligence Semantic Model
BI APPLICATIONS
SSRS
EXCEL
3RD PARTY APPS
MULTIDIMENSIONAL
TABULAR
DATA MODEL
MDX
DAX
BUSINESS LOGIC
ROLAP
MOLAP
DIRECT QUERY
DATA ACCESS
xVelocity
LOB
CLOUD
DATA SOURCES
RDBMS
FILE

5. A Brief History of Tabular
2 Internal papers in 2006 by Amir Netz about a BI Sandbox and In-memory technology
Power Pivot for Excel was first released as an add-in in May 2010 as part of the SQL Server 2008 R2 release
SQL Server 2012 saw the introduction of a completely new data model, SQL Server Analysis Services Tabular model
Microsoft’s entry into the Self-Service BI market.

6. TABULAR BENEFITS OVER POWER PIVOT
Power Pivot vs Tabular
SCALABILITY
SECURABILITY
MANAGEABILITY
TABULAR BENEFITS OVER POWER PIVOT
PowerPivot has essentially become the desktop version of Analysis Services, but remains an excellent prototyping tool for Tabular.

7. Tabular vs Multidimensional
EASIER, QUICKER, NEWER TECHNOLOGY
EXCELLENT QUERY PERFORMANCE
VARIED DATA SOURCES
CALCULATED COLUMNS/TABLES
MULTI-DIMENTIONAL
PROVEN, STABLE, OLDER TECHNOLOGY
VERY LARGE DATABASES (TERABYTES)
MORE GRANULAR SECURITY
WRITEBACK, AGGREGATIONS, ACTIONS, NAMED SETS

8. xVelocity In Memory Analytics Engine
Compression algorithm
Compression techniques
Less RAM usage and decreased costs
Increased performance as less data to scan
Dictionary/Hash Encoding
Value Encoding
Run Length Encoding
Originally called the VertiPaq engine, xVelocity is an in-memory columnar database
where all of the data in a model lives in RAM.
Columnstore indexes use this same compression technology.

9. xVelocity Dictionary Encoding
A dictionary of unique values in a column is built and then data values are replaced with the values of the dictionary index.
Regardless of datatype the same performance gains are achieved.
SIZE
Small
Extra Large
Medium
Extra Small
Large
SIZE ID 1 2 3 4 ID
SIZE
Small 1
Extra Large 2
Medium 3
Extra Small 4
Large

10. xVelocity Value Encoding
xVelocity looks for mathematical relationships between the values in a column, then uses the value itself, with or without a mathematical operation applied, to decrease the memory usage.
PRODUCT_KEY
100023
100024
100025
100026
100027
100028
100029
Provides better performance as computations can operate directly on the data without a separate lookup step as with dictionary encoding.
Product_key 23 24 25 26 27 28 29
Less storage used for smaller value by subtracting
Adds the value back in when required.

11. xVelocity Run Length Encoding
Complementary compression technique that tries to reduce the size of a dataset by avoiding repeated values.
quarter Q1
….. Q2 …
Values are replaced with a slightly more complex structure that contains the value only once, with the number of contiguous rows having the same value.
Repeated 210 times
QUARTER
START
COUNT Q1 1
210 Q2
211
260
Repeated 260 times
Example from Ferrari, Alberto; Russo, Marco. The Definitive Guide to DAX: Business intelligence with Microsoft Excel, SQL Server Analysis Services, and Power BI (Business Skills) (p. 411). Pearson Education. Kindle Edition.

12. xVelocity Re-encoding
SAMPLE
Read a sample of the data
CHOOSE
Choose a compression technique based on sample
COMPRESS
Compress the data using chosen technique
RE-ENCODE
Determine a new compression technique, if required
Provide a good sample of data when doing an initial partition process to keep re-encoding to a minimum.

13. xVelocity Relationships
To improve performance, relationships between tables are stored in memory as pairs of ID’s and row numbers.
Sale amount
Product id 10 2 12 3 34 7
Product id
product 2
Nutty Kitty 3
Chocolate Bunny 7
Nutty Chocolate Bunny
ROW 1
ROW 2
Given the ID in one table, it can immediately find the corresponding rows in the joined table.
ROW 3
Sales[product id]
Product[row number] 2 1 3 7

14. xVelocity Design Considerations
A column can have both Run Length Encoding AND Dictionary OR Value encoding.
1. CARDINALITY
Number of unique values in the column, lower is better
2. DISTRIBUTION
Repeated values have higher compression rates than those columns with frequently changing values
DESIGN CONSIDERATIONS
3. TABLE SIZE
Number of rows in the table
4. DATA TYPE
Affects the dictionary size

15. DirectQuery REAL TIME REPORTING VERY LARGE DATABASES
DAX CONVERTED TO SQL
DIRECTQUERY
SEMANTIC LAYER
SINGLE RELATIONAL SOURCE
DIFFERENCES IN DAX FORMULAS AND SUPPORT

16. Partitioning
Partitions divide a table into logical parts and provides several benefits.
DECREASED PROCESSING TIME
EASILY REMOVE OR UPDATE DATA
CONSOLIDATE DATA FROM VARIOUS SOURCES
amount
date
220 ….
…..
468
1290 … 34 3 98
Partition for 2015
Partition for 2014
Partition for 2013

17. Processing – Full, Add, Recalc, Default
REFRESHES ALL STRUCTURES
AVAILABLE AT DATABASE, TABLE AND PARTITION LEVEL
MODEL IS QUERYABLE THROUGHOUT
ENTIRE COPY OF DATABASE STORED IN MEMORY
ADD
UPDATES PARTITION WITH NEW DATA
AVAILABLE AT PARTITION LEVEL
MODEL IS QUERYABLE THROUGHOUT
NO AUTOMATIC DETECTION OF DUPLICATES
RECALC
UPDATES AND RECALCULATES
AVAILABLE AT DATABASE LEVEL
NEEDS TO BE MANUALLY ISSUED AFTER PROCESS DATA
AFFECTS HIERARCHIES, CALCS AND RELATIONSHIPS
DEFAULT
MAKES OBJECT QUERYABLE
AVAILABLE AT DATABASE, TABLE AND PARITION LEVEL
ONLY OPERATIONS NECCESARY TO MAKE OBJECT QUERYABLE
CAN RUN PROCESS DATA AND OR PROCESS RECALC

18. Processing - Data, Clear, Defrag
PROCESS DATA
LOADS DATA ONLY
AVAILABLE AT TABLE AND PARTITION LEVEL
MODEL IS NOT QUERYABLE AFTERWARDS
RUN PROCESS DEFAULT OR PROCESS RECALC AFTER
PROCESS CLEAR
DROPS ALL DATA
AVAILABLE AT DATABASE, TABLE AND PARTITION LEVEL
MODEL IS NOT QUERYABLE AFTERWARDS
NEED TO LOAD DATA AND RECALC
PROCESS DEFRAG
REBUILDS TABLE DICTIONARY
AVAILABLE AT TABLE LEVEL
USED WHEN PARTITIONS FREQUENTLY CHANGE
USELESS AFTER PROCESS FULL OR PROCESS DATA
SQL Server 2016 Analysis Services (SSAS) includes parallel processing for tables with two or more partitions.

19. Automating with ASSL, TMSL & AMO
Analysis Services Scripting Language (ASSL) is an extension of the XMLA Open Standard and can be used to automate admin tasks.
Tabular Model Scripting Language (TMSL) – JSON. New in SQL Server 2016 with relevant Tabular language elements.
Analysis Management Objects (AMO) – Managed Interface used to run XMLA, MDX etc.
ASSL
TMSL
AMO

20. Basic Tabular Demo
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21. ? The Future MERGED TECHNOLOGIES TABULAR REPLACES MULTI-DIMENSIONAL
HYBRID SOLUTIONS
From version 2008 R2 onwards there have been no major updates added to the multidimensional model – this could be because it is a stable technology. But they are adding more and more features to the tabular model. For the 2016 release version Microsoft added loads of features to Tabular. Tabular model is also closely related to Excel and the xVelocity technology is being used by PowerPivot, Analysis Services Tabular, SQL Server Columnstore and PowerBI, Microsoft is clearly investing more in tabular which makes them more competitive against other in-memory technologies like SAP HANA, Tableau, QlikView etc. Even newer BI players like Pyramid with their offering of BI Office are using Tabular as a back-end.
The only data source for Power View is a Tabular database or PowerPivot workbook.
I’ve read a lot of articles about where analysis services is going, and opinions range from, ssas eventually merging multidimensional and tabular into a single product to multidimensional being dropped altogether. Personally I think there is space for both technologies, and we could expect hybrid solutions. There is too much investment in Multidimensional, and it is a stable technology, so I don’t see it going anywhere for some time.
One thing seems fairly likely, that SQL Server Analysis Service will be offered as a service and will be Azure based at some point, and Tabular will be the choice.
Also, as soon as Tabular is up to date with current expectation and it can offer full functionality, I can only see multidimensional being used in very specific circumstances, and not widely.
ANALYSIS SERVICES AS A SERVICE (AZURE)

22. References http://sqlblog.com/
Ferrari, Alberto; Russo, Marco; Webb, Chris. Microsoft SQL Server 2012 Analysis Services: The BISM Tabular Model (Developer Reference) Pearson Education. Kindle Edition.
model-bism/
Credit: Business Intelligence in Microsoft SharePoint 2010 By Norm Warren, Mariano Neto, John Campbell, Stacia Misner
Ferrari, Alberto; Russo, Marco. The Definitive Guide to DAX: Business intelligence with Microsoft Excel, SQL Server Analysis Services, and Power BI (Business Skills) (p. 411). Pearson Education. Kindle Edition.

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