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 Small
Marcus 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)

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 Startups
How 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
Video
Audio
DemandContent
GPS
Customer Data
ServiceCalls
Emails
Virtual Goods
Social MediaMobile
InstantMessages
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 Program
What 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 Access
Technology
 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….

SAP HANA
Crunching 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
GPS
Demand
Speed
Velocity
Transactions
Opportunities
Servicecalls
Customer
Sales orders
Inventory
E-mails
Tweets
Planning
Things
Mobile
Instantmessages
VELOCITY
VOLUME VARIETY

Algorithmic Challenge
Challenges
Forecasting
Key
Influencers
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-memory
Compression
SQL interface on
columns & rows
SQL
T
Text
Engine

Data Challenges
How 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

 SAP HANA supports rows, but is optimized for column-order data organization
Order Country Product Sales
456 France corn 1000
457 Italy wheat 900
458 Italy corn 600
459 Spain rice 800
Column and Row Store
457 Italy wheat 900
458 Italy corn 600
459 Spain rice 800
456
457
458
459
France
Italy
Italy
Spain
corn
wheat
corn
rice
1000
900
600
800
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
Main Memory
at Blade i
Log
SnapshotsPassive Data (History)
Non-Volatile
Memory
RecoveryLogging
Time
travel
Data
aging
Query Execution Metadata TA Manager
Interface Services and Session Management
Distribution Layer
at Blade i
Main Store Differential
Store
Active Data
Merge
Column
Column
Combined
Column
Column
Column
Combined
Column
Indexes
Inverted
Object
Data Guide

Order Country Product Sales
457 Italy wheat 900
458 Spain rice 600
459 Italy rice 800
460 Denmark corn 500
461 Denmark rice 600
462 Belgium rice 600
463 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 Belgium
2 Denmark
3 France
4 Italy
5 Spain
1 3
2 4
3 5
4 4
5 2
6 2
7 1
8 4
… …
1 7
2 5,6
3 1
4 2,4,8
5 3
Logical Table
Dictionary
5 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 query operation, using
multiple cores / threads
Transparent to developer
Parallelization
Inter Transaction Intra Transaction
Inter Query Intra Query
Inter Operation Intra Operation
Pipeline
Parallelism
Data Parallelism
Pipeline
Parallelism
Data Parallelism
Parallelism

Algorithmic Challenges
Going Beyond Descriptive

Extending your analytics capabilities
ANALYTICS MATURITY
LEVELOFINSIGHT
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 Analysis

Presentation Challenge
Design 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
Explorer
Version 9
Version 8
Chrome
Latest
version
Firefox
Version 3.6
and latest
version
Safari
Latest
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

Thank you!
Sönke Moosmann
SAP Innovation Center
soenke.moosmann@sap.com
Marcus Krug
SAP Innovation Center
marcus.krug@sap.com

The SAP Startup Focus Program – Tackling Big Data With the Power of Small by Soenke Moosmann

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