Introducing Microsoft

SQL Server 2016 R Services

Julian Lee

Advanced Analytics Lead

Global Black Belt – Asia Timezone

Consistent experience from on-premises to cloud

Microsoft Tableau Oracle

$120

$480

$2,230

Self-service BI per user

In-memory across all workloads

built-inbuilt-in built-in built-in built-in

at massive scale

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SQL Server Oracle MySQL SAP HANA TPC-H

Oracle is #5#2

SQL Server

#1

SQL Server

#3

SQL Server

SQL Server 2016: Everything built-in

2

From data to decisions and actions

Value

Data

$1.6trillion

ActionDecisions

Microsoft Advanced Analytics Offerings

Cortana

Analytics Suite

SQL Server 2016

Typical advanced analytics lifecycle

Ingest Transform Explore Model Deploy

Score Visualize Measure

Model

Score

ƒ(x)

Preparation Modeling

Operationalization

Data Scientist should be creating / testing models

Ingest Transform Explore Model Deploy

Score Visualize Measure

Model

Score

ƒ(x)

Preparation Modeling

Operationalization

But the reality is different …

15%

Data scientist focus time

Ingest Transform Explore Model Deploy

Score Visualize Measure

Model

Score

ƒ(x)

Preparation Modeling

Operationalization

80%

5%

15%

R – What is it?

Open source “lingua franca”

Analytics, computing, modeling

Global community

Millions of users 7,000+Packages

Big dataEcosystem

Scalability

CRAN: The Comprehensive R Archive Network

Open Source “lingua franca”

Analytics, Computing, Modeling

In addition to CRAN, Bioconductor, GitHub, and others distribute R packages

Large talent base knows how to use it already

R – Why use it?

Scalability for ongoing computation of data

Protecting important data is much easier

Dividing use between roles creates efficiency

$?

Challenges posed by open source R

Uncertain total cost of ownership and return on investment

Integrating R with existing and ever changing data infrastructures

Scale and Performance

Data movement restricts access for efficient data modeling

24/7 support,

enterprise-grade platform

Included in SQL 2016

Single Analytics platform on multiple data infrastructures

Smarter decisions,

faster analysis and results

Hybrid cloud,

reusable code,

fewer limits

SQL Server 2016 R Services offers

In-database advanced analytics

Data Scientist

Interacts directly with data

SQL Developer/DBAManage data and

analytics together

ExtensibilityExample solutions

Sales forecasting

Warehouse efficiency

Predictive

maintenance

Credit risk protection

010010

100100

010101

Relational data

Analytics library

T-SQL interface

?R

integration

Built into

SQL Server 2016

010010

100100

010101

Real-time operational analyticswithout moving data

R with in-memory scalability

SQL Server R Services:

Model , Deploy & Operationize

In SQL16:Support Entire Analytics Lifecycle

Enable R Users to Run R Inside SQL 2016

Enable SQL Users to Extend BI Applications Using

R Analytics

Advantages:Scale By Eliminating Movement

Scale Using Parallelized Analytics

Reduced Security Exposure

SQL Skill Reuse for Data Engineering

SQL Skill Reuse for App development

Improved Operational Stability for Applications

Enterprise R Analytics

Typical Advanced Analytics Process

OperationalizeModelPrepare

SQL 2016

Improving advanced analytics with R and SQL

Server

Data Scientist

Publish algorithms and interact directly with data

Run R across entire data set

Execute and test in-database

Deploy to local database

SQL Developer

Better manage data and analytics together

Operationalize R script/model

Use T-SQL constructs

DBA

Better manage storage and analytics together

More easily maintain performance and stability

Secure and govern R runtime execution

Custom parallelization

PEMA-R API

rxDataStep

rxExec

Data step

Data import – Delimited, fixed, SAS, SPSS, OBDC

Variable creation & transformation

Recode variables

Factor variables

Missing value handling

Sort, merge, split

Aggregate by category (means, sums)

Descriptive statistics

Min/max, mean, median (approx.)

Quantiles (approx.)

Standard deviation

Variance

Correlation

Covariance

Sum of squares (cross-product matrix for set variables)

Pairwise cross tabs

Risk ratio & odds ratio

Cross-tabulation of data (standard tables & long form)

Marginal summaries of cross tabulations

Statistical tests

Chi Square Test

Kendall Rank Correlation

Fisher’s Exact Test

Student’s t-Test

Sampling

Subsample (observations & variables)

Random sampling

Predictive models

Sum of squares (cross-product matrix for set variables)

Multiple linear regression

Generalized linear models (GLM) exponential family distributions: binomial,

Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions: cauchit,

identity, log, logit, probit. User defined distributions & link functions.

Covariance & correlation matrices

Logistic regression

Classification & regression trees

Predictions/scoring for models

Residuals for all models

Simulation

Simulation (e.g., Monte Carlo)

Parallel random number generation

Cluster analysis

K-Means

Classification

Decision trees

Decision forests

Gradient-boosted decision trees

Naïve Bayes

Parallelized, remote executing algorithms

Faster and more scalable

Appendix

Want to know more?

Find out how much R knowledge is already in-house

Contact your Microsoft Data Platform Specialist or local Black Belt

Learn more about the relationship between Microsoft and Revolution Analytics

http://microsoft.com/SQL

http://blogs.technet.com/b/machinelearning/archive/2016/01/12/making-r-the-

enterprise-standard-for-cross-platform-analytics-both-on-premises-and-in-the-cloud.aspx

5 Key Hurdles for Advanced AnalyticsLoad Data Re-CodeBuild Insight

Innovation Rate

?

MapReduce YARN Spark ?

< 3 years

Rapid Big Data Evolution

Large Unknown Current Spend

? = ∑( )€

£

$

€$

Rp¥$

Complex Infrastructure Decisions

Cloud

Security

Experience

Transition Cost

Risk

Agility

Elasticity

TCO

Fit

On-Prem

?

DataScientist

Analyst

Business Executive

Developer

Data Steward

Data Engineer

Data Lake

Decisions

Applications

Expanding Communities

Demo Link

http://13.76.242.39/