the sap startup focus program – tackling big data with the power of small by soenke moosmann

The SAP Startup Focus Program – Tackling Big Data With the Power of SmallMarcus Krug and Sönke Moosmann, SAP Innovation Center

© 2013 SAP AG. All rights reserved. 2Public

Agenda

SAP Innovation Center Why Startups Matter To Us? SAP Startups Focus Program (SFP)

SAP Innovation Center


Who We Are

Established in February 2011

First of its kind for SAP AG

Now: 35 FTEs

Soon: 100 FTEs plus 200

students


Looking Beyond ERP

Personalized Medicine

Online Gaming

Film Production

Smart Energy

Supply-Chain

Innovation

Technologies In-Memory-

Technology Cloud Mobility

SAP and StartupsHow Does That Fit Together?


Some Might See Us as…

Grow old

Slow

BUT TURTLES ARE ALSO…

Quite „fundamental“, according to ancient Asian mythology


Commonalities - Innovation….

Business(viable)

Technology(feasible)

Innovation

Human Values(desirable,usable)


BIG Data

VideoAudio

De

ma

nd

Co

nte

nt

GP

S

Customer Data

Se

rvice C

alls

Emails

Virtual Goods

Social MediaMobile

Instant Messages

SAP HANA


BIG Ambitions

SAP Strategic Goals 2015 20 Bn in Revenue 35% Margin 1,000,000,000 Users

→ Dramatically extend SAP‘s ecosystem

And if we join forces…


This is What Will Happen…

SAP Startup Focus ProgramWhat Startups Can Expect From Us


Access…1. Technology Many startups face „big data“ and „real-time“ challenges → SAP

HANA

2. Customers SAP Install Base of close to 200,000 customers across 25 industries

3. Financing SAP Ventures


SFP – All About AccessTechnology SAP HANA One Developer Edition (free) → 1 year per default (flexible) Physical and virtual HANA bootcamps, HANA virtual learning platform technical advisor

Joint Go-to-Market Appearances at SAP and non-SAP events Solution showcases on HANA marketplace Dedicated GTM advisor → GTM plan Pipeline creation to drive SFP startups‘ revenue

Access to SAP Ventures and other VCs SAP Ventures and other VCs (155 Mio $ HANA Real-time Fund)


SFP – From Idea to (Market) Impact

Attend Startup Forum

Development Accelerator GTM

Boot Camp

SFP ≈ 1 year ≥1 year: commercial state

Market-ready solution

SFP selection

Pitch at Forum


SFP – Then, Now and Beyond

March - May 2012

- The first 10 startups

- Recruited from friends + family

- High touch

March – Sept 2012

- From 10 -100

- startup forums held globally

- HANA boot camps

- HANA developer edition on AWS

Sapphire EMEA 2012

- ≈150 startups

- 50 with Proof-of-Concept

2013 – Transition to Scale!As of now:- 200+ startups in SFP- 60+ productive solutions

Goals 2013

- 1000+ startups in SFP

- 200+ productive solutions


Some References

(Israel)(US) (US)

(France)

(UK)(UK)(Israel)

(US)

(Canada)

(US) (US) (Germany)

etc….

http://www.liquidanalytics.com/

http://fanappz.com/

http://www.mobideo.com/

http://qunb.com/

http://www.warwickanalytics.com/

http://www.weissbeerger.com/index.html

http://www.hyperbees.com/

http://www.coremobilenetworks.com/

http://www.optessa.com/

SAP HANACrunching Big Data Made Easy


The Microscope :: A Tool for Biological Exploration

Before the invention of the microscope Difficult to study tiny structures Only models, hypotheses about

Cells Micro-organisms

Difficult to verify / falsify hypotheses

After the invention of the microscope Tiny structures are plain to see Can be studied in real time

Challenges... and how HANA helps tackle these


Data Challenge

CRM* data

GP

S

Demand

Spe

ed

Velocity

Transactions

Opp

ortu

nitie

s

Service

calls

Customer

Sales orders

Inventory

E-m

ails

Tweets

Planning

Things

Mobile

Insta

nt m

essa

ge

s

VELOCITY

VOLUME VARIETY


Algorithmic Challenge

Challenges

Forecasting

KeyInfluencers

Trends

Anomalies

Relationships


Presentation Challenge


Application Server

SAP HANA Overview

Predictive analytics

Scripting

Data Modeling

R Integration

Math Libraries

Column and

row store

+

Multi-core/parallelization

In-memoryCompression

SQL interface on columns & rows

SQL

T

Text Engine

Data ChallengesHow do you crunch big data in real time?


Optimizing Data Access Patterns

Challenge: Data locality! Yes, DRAM is 100,000 times faster than disk… But DRAM access is still 4-60 times slower than on-chip caches

60 -100ns


SAP HANA supports rows, but is optimized for column-order data organization

Order Country Product Sales456 France corn 1000457 Italy wheat 900458 Italy corn 600459 Spain rice 800

Column and Row Store

456 France corn 1000

457 Italy wheat 900

458 Italy corn 600

459 Spain rice 800

456457458459

FranceItalyItalySpain

cornwheatcornrice

1000900600800

Row order organization

Column order organization

Single-record access:SELECT * FROM SalesOrders WHERE Order = ‘457’

SQL

Single-scan aggregation:SELECT Country, SUM(sales) FROM SalesOrders WHERE Product=‘corn’ GROUP BY Country


Combining OLTP and OLAP

Write operations are accumulated in a dedicated data structure (delta store)

Write operations are insert-only! Integration of differential data in async.

merge process.

MVCC enables processing of OLTP workloads

Insert only approach favors implementation of MVCC


Order Country Product Sales

456 France corn 1000457 Italy wheat 900458 Spain rice 600459 Italy rice 800460 Denmark corn 500461 Denmark rice 600462 Belgium rice 600463 Italy rice 1100… … … …

Columnar Dictionary Compression

Dictionary per column Uses data-driven fixed-length bit encodings Operations directly on compressed data, using integers More in cache, less main memory access

1 Belgium2 Denmark3 France4 Italy5 Spain

1 32 43 54 45 26 27 18 4… …

1 72 5,63 14 2,4,85 3

Logical Table

Dictionary5 entries, so need 3 bits to encode!

Compressed column

(bit fields)Inverted

indexDictionary

Where was order 460?

Which orders in Italy?


More Columnar Compression Techniques


Concurrent users

Concurrent operations within a query

Data partitioning, on one host or distributed to multiple hosts

Horizontal and vertical parallelization of a single queryoperation, using multiplecores / threads

Transparent to developer

Parallelization

Inter Transaction Intra Transaction

Inter Query Intra Query

Inter Operation Intra Operation

Pipeline Parallelism

Data ParallelismPipeline

ParallelismData Parallelism

Parallelism

Algorithmic ChallengesGoing Beyond Descriptive


Extending your analytics capabilities

ANALYTICS MATURITY

LEV

EL

OF

INS

IGH

T

Sense & Respond Predict & Act

Raw Data

Cleaned Data

Standard Reports

Ad Hoc Reports & OLAP

Generic Predictive Analytics

Predictive Modeling

Optimization

What happened?

Why did it happen?

What will happen?

What is the best that could happen?


SAP HANA Modeling

Analytical View Attribute View Column Table

Calculation View


Implementing Predictive Analytics

SQLScript set of SQL extensions to push data-intensive logic into the database and leverage parallel

execution strategies of the database

PAL (Predictive Analysis Library) Built-in C++ statistical and data mining algorithms K-means, k-nearest neighbor, decision trees, multiple linear regression, classification, and many

more

R integration Leverage R’s 3000+ external packages to perform wide-range data mining and statistical

analysis.


Intuitively Design Predictive Models using Predictive Analysis


SAP Predictive AnalysisOther visualizations in the gallery

Presentation ChallengeDesign HANA apps easy and fast


SAP HANA XS Engine

Rationale: Enable application development and deployment – minimize layers HTTP-based UI (browser, mobile apps) Runs directly on HANA, minimizes TCO Leverages built-in strengths of SAP HANA

for the best possible performance

Scope From lightweight environment for small web-

based applications To robust environment for complex high-

speed business applications

Control flow logic

Calculation logic

Data

Clients

Presentation logic

HANA

XS


Implement UIs with SAPUI5

SAPUI5 is an extensible JavaScript-based HTML5 browser rendering library for Business Applications.

Uses the jQuery library as a foundation

Open AJAX compliant and can be used together with/uses other standard JS libs

Supports RIA like client-side features based on JavaScript

Supports an extensibility concept regarding custom controls

Allows usage of own JavaScript and HTML

Internet ExplorerVersion 9Version 8

ChromeLatest version

FirefoxVersion 3.6 and latest version

SafariLatest version


SAPUI5 Templates


Demo :: In-Game Promotion Management

Bigpoint

Europe‘s largest browser game provider with 300 Mio users

Revenue through selling „virtual goods“

1-3% of users are buying virtual offers.

Goals

Perform real-time massive amount of event stream analytics

Filter and monitor online players

Enabling in-game promotion offers and track results in real-time


In Summary

In-memory data management Column-oriented data layout Compression Parallelization Optimized for big data Transparent to developer

HANA Applications XS Engine Application Server Control logic SAPUI5 Reduced TCO

Predictive Analytics SQLScript R integration Predictive Analysis

Library Unlock new insights


Take Your Chance!

The SAP Startup Forum is coming to Berlin again!

When: June 19th Where: SAP Office Berlin

(Rosenthaler Str. 30)

For more info, visit our event website: http://www.saphana.com/community/learn/startups/forums/berlin

Or turn to us directly

http://www.saphana.com/community/learn/startups/forums/berlin

http://www.saphana.com/community/learn/startups/forums/berlin

Thank you!

Sönke MoosmannSAP Innovation [email protected]

Marcus KrugSAP Innovation [email protected]

the sap startup focus program – tackling big data with the power of small by soenke moosmann

Technology

sap ag

sap startups

customers sap

accesstechnology sap

public sap hana

vcs sap ventures

financing sap ventures

sap events solution