Let’s Build a Tabular Model in Azure Analysis Services is in Azure? Seriously!? Let's build a model! 2016-04-08 Phillip Labry

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Demo

Phillip Labry Sr. BI Consultant at Pragmatic Works IT development for over 30 years Developer, DBA, Business Intelligence Experience with Manufacturing, Telecom, Banking, Retail, Government, Insurance, Healthcare, Consulting, Energy, Finance,Logistics Twitter: @phillipLabry Blog: http://phillip.labry.com

Today’s scenario

Basic Terms Measure Numeric value that can be aggregated (Sales Amount) Fact Collection of fields mainly consisting of Measures Dimension Table of values that describes a fact (people, places, things) Star Schema Dimension tables radiating out from a related fact table Snowflake Schema Dimensions related to other dimensions Aggregate A mathematical summarization of measures Attribute Another name for Column(used in Dimensions)

What is Analysis Services? Analytical database designed to be business facing Optimized for aggregating huge data sets Two engines: OLAP and Tabular

Benefits of Azure Analysis Services Scale up, scale down and pause No hardware required Inherent redundancy Explore data from anywhere Use the tools you love and know SSMS, SSDT, Power BI, Excel

Why Azure Analysis Services? Upsize data from Power BI Faster data refreshes Adjust for peak workloads New features quicker Save on hardware costs, IT infrastructure

Model Types In Memory Direct Query

Supported data sources On Premises SQL Server PDW / APS Oracle Teradata Cloud Azure SQL Database Azure SQL Warehouse

BI Semantic Model: Vision (2012) Data model Business logic and queries Data access ROLAP MOLAP xVelocity Direct Query MDX DAX Multi- dimensional Tabular Third-party applications Reporting Services Excel PowerPivot Databases LOB Applications Files OData Feeds Cloud Services SharePoint Insights Power View

BI Semantic Model (Azure Analysis Services)

For Development DO NOT CHOOSE PRODUCTION SERVER FOR WORKSPACE Use Developer Tier pricing Remember to pause your machines

Dimensions Wide and shallow Describe facts Can contain hierarchies Can contain calculated columns

Hierarchies Predefine common hierarchies for the users Hierarchies are defined from largest group to smallest Year Quarter Month Hide columns used for hierarchies where appropriate

Fact Tables Deep and narrow Mostly measures(Numbers) Keys to dimensions(Ints) Natural repository for calculated measures

Calculated columns and Measures Created in the model only Calculated measures execute when called based on filter context Calculated columns are created on data load and persist in memory

Requirements Azure account Azure Active Directory Tenant(MS live accounts are not supported) Directory integration between AAD and on premises AD is recommended but not required Resource Group Create a server

Demo

Current Pricing QPUS MEMORY (GB) Hourly Price Monthly Price S0 40 10 $0.605/hr ~$450.12 S1 100 25 $1.015/hr ~$755.16 S2 200 50 $2.03/hr ~$1,510.32 S4 400 $4.055/hr ~3,016.92 Developer 20 3 $0.066/hr ~$49.11

Tips for development Clean table names on first import Settle on column names before creating any calculated columns or measures Flatten out snowflakes where possible Avoid creating calculated columns for intermediate measures Use views for source data Use meaningful and verbose names Use attribute properties and formatting

Current Azure Analysis Services challenges Preview only Backups Limited source data No Power BI connectivity

Additional resources

Thank you @PhillipLabry