in memory technology hana

In-Memory Computing:

Realized Business Benefit and the Road Ahead

Noam Berda, Business Development Manager, Business Analytics & Technology

SAP Asia Pacific Japan

© 2011 SAP AG. All rights reserved. 3

Innovation Drives Success

Technology Innovations Enable Businesses to Become…

More flexible and

quick to action with

proper insight

Fiscally and operationally

efficient

Empowered at the

business user level to

make smart decisions

and act on these

demands


In-Memory Computing

In-Memory Computing

Technology that allows the processing of

massive quantities of real time data

in the main memory of the server

to provide immediate results from

analyses and transactions


Poll Question

By 2012 what would be the percentage of organizations (global 1000) that loads

data into in-memory technology for BI performance optimization?

• 5%

• 40%

• 70%


Gartner

AGENDA

© 2010 SAP AG. All rights reserved. / Page 7

1. SAP’s In-Memory Computing Technology

2. How do you benefit?

3. SAP’s In-Memory Computing Offerings

4. Example in-memory computing scenario


Don’t Bet on Database Performance

Reality Dictated by Physics

Improvement20101990

216Addressable

Memory

250x5MB/$

0.02MB/$

Memory

143x7.15MIPS/$

0.05MIPS/$

CPU

Technology Drivers

130MBPS

5MBPS

Disk

Data Transfer 25x

100x10Gbps

100Mbps

Network Speed

264 248x

Performance will continue to be an issue for analyzing large amounts

of information


Discrete

The Inflection PointIn-Memory Computing

Hardware

Multi-core architecture

Massive parallel scaling

64-bit address space

Upto 2TB main memory

100GB/s data throughput

Row and

Column Store

Compression

Partitioning

Virtually unlimited size

Fast prefetch

Volatile and/or persistent

No Aggregate

Tables

Insert Only

on Delta

Software Today Tomorrow

10X compression

Massively parallel

processing

Cache

Disk

++

+ +

+

MemoryIn-Memory

Disk

Integrated


SAP In Memory Solutions Available Today

SAP Applications Powered by In-Memory Technology

Today

SAP NetWeaver BW Accelerator (BWA)

SAP BusinessObjects Explorer, accelerated

SAP CRM Customer Segmentation

SAP Business ByDesign analytics

SAP Advanced Planner and Optimizer

SAP NetWeaver Enterprise Search


Evolution of In-Memory at SAP

SAP NetWeaver®

BI Accelerator 7.0

2005

SAP NetWeaverBW Accelerator 7.20

2008

SAP BusinessObjectsExplorer Accelerated

2009

HANA 1.0

2010

HANA 1.5

2011

today

* Dates relates to RTC (release to customer)


In-Memory Analytics in Action

Retail Customer – Acceleration of Complex Reports

Average improvement 96.7% improvement in

reporting time

Loading 1.1 million records/day with 36

months of history

Tetra Pak External

Global Information Management

Chris Rowley / 31 July 2008

14

From Department to Enterprise BI Business user adoption is the measure of success

2008 BI User Adoption

Tetra Pak External

Global Information Management

Chris Rowley / 31 July 2008

15

Successful Selling Strategies

http://www.caltex.com.au/index.asp

http://www.supercheapauto.com.au/default.aspx


Preconfigured Analytical Appliance

■ In-Memory software + hardware

(HP, IBM, Fujitsu, Cisco)

In-Memory Computing Engine Software

■ Data Modeling and Data Management

■ Real-time Data Replication Data Services for SAP

Business Suite, SAP BW and 3rd Party Systems

Capabilities Enabled

■ Analyze information in real-time at

unprecedented speeds on large volumes of non-

aggregated data

■ Create flexible analytic models based on real-

time and historic business data

■ Foundation for new category of applications

(e.g., planning, simulation) to significantly

outperform current applications in category

■ Minimizes data duplication

SAP High-Performance Analytic Appliance (SAP HANA)Architecture

BICS SQL MDXSQL

Modeling

Studio

Real–Time

Replication

Services

Data

Services

SAP HANA

SAP BusinessObjects Other Applications

SAP NetWeaver

BW

SAP Business

Suite3rd Party

In-Memory Computing Engine

Calculation and

Planning Engine


Technical Overview (1 of 2)

Calculation models – Extreme Performance and Flexibility with Calculations on the fly

Calculation Engine

Calculation Model

Distributed Execution Engine

Row Store Column Store

SQL MDXSQL

Script

Plan

Modelother

Compile & Optimize

Physical Execution Plan

Logical Exection Plan

Parse

SAP in-memory computing engine

Calculation Model

Artifacts from domain specific languages

(SQL, MDX, etc) get translated into a

common representation (calc model)

A calc model is a directed graph

representing the flow of data from input to

output passing various operations

A calc model can be generated on the fly

based on an expression provided by a client

(Excel providing an MDX)

A calc model can also define a

parameterized calculation schema for highly

optimized reuse

A calc model supports scripted operations

© 2011 SAP AG. All rights reserved. 18This presentation and SAP‘s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is

provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of m erchantability, fitness for a particular purpose, or non-infringement

ABAP AS

App

DB

ABAP AS Next Generation

Next Generation Apps

SAP HANA

Data in

memoryRuntime

Procedure

code

Program

code

compile

& deploy

Fast data

transfer

Technical Overview (2 of 2)

Applications – Tight coupling between application server and SAP HANA

Today Mid-Term (Plan)Tight Coupling

With large data volumes,

reading information

becomes a bottleneck

Next generation

applications will delegate

data intense operations

The runtime environment

executes complex

processes in memory

In memory computing

returns results by pointing

apps to a location in

shared memory

AGENDA







154,000customers

SAP HANA in Action at a CPG Company

Dunning Process Acceleration

1.8M 1,00070,000rows of data

B2B customers

collection notices 13 seconds

77 minutes

Standard System In-Memory System

356xfaster


SAP HANA for Data Intensive Point of Sale Analysis

Large CPG company wants to

analyze all their POS of data to

predict demand

Target -stock shelves with 48 hour

turn-around

Data Set is 460 Billion records (40

Terabytes)

Unable to analyze data using

current database platform

10 HANA blades with 500GB per

blade & 2TB SSD Storage

HW Cost = $532K

SAP BusinessObjects Explorer

120 TB in traditional system =

40TB Hana

20x Faster Analysis with 200x

Better Price/Performance

Moved from 5 days down to 2

days for shelf turnaround

Eliminates out of stock

scenarios during promotions

Challenge ResultsSolution


NRI Japan

Real Time Traffic Analysis by geo and time- Collecting location data of over 12,000 contracted taxis (15m rec/ day).

- World first probe data-based real-time navigation service.

- Drive less be more efficient.

- Reduce hazards and traffic congestion.

Why SAP HANA?- Existing DB cannot handle such high volume of analysis in real-time.

- HANA can deliver real-time analysis on large amounts of data.

APJ HANA Win Story

http://www.nri.co.jp/index.html

AGENDA







SAP ECC

4.6c / 4.7 / ECC 6.0(or CRM, SRM, SCM)

Traditional DB

Oracle, DB2, SQL

Server, MaxDB

EDW

SAP BW / Custom

Traditional

DBBWA

BI

D

D

D

Data

Marts BI

ETL

Update

Daily

Today’s System – After Event Analysis


SAP ECC


Traditional DB

Oracle, DB2, SQL

Server, MaxDB

EDW

SAP BW / Custom

Traditional

DBBWA

BI

D

D

D

Data

Marts BI

ETL

Update

Daily

HANA 1.0

~10x compression

100x faster analysisNear Real-time

BI

Step 1. Install and Run HANA in parallel with ECC

Q4

2010

Any Data

Real-Time Operational Analytics


SAP ECC


Traditional DB

Oracle, DB2, SQL

Server, MaxDB

SAP BWBI

H

Data

Mart BI

HANA 1.5

~10x compression

100x faster analysisNear

Real-time

BI


Step 2. Use HANA as in-memory EDW and Datamart for All Data

Any Data

SAP

Non-SAP

Accelerated

Accelerated

2011In-Memory EDW/Data Mart + Accelerated BI


SAP ECC


Traditional DB

Oracle, DB2, SQL

Server, MaxDB

SAP BWBI

H

Data

Mart BI

HANA 1.5

~10x compression

100x faster analysisNear

Real-time

BI



Step 3. Deliver new In-Memory Applications (e.g. BPC, Demand Planning)

Any Data

SAP

Non-SAP

Accelerated

Accelerated

Applications

2011In-Memory Applications – Redefine Planning


SAP ECC


SAP BWBI

H

Data

Mart BI

HANA 2.0

~10x compression

100x faster analysis

BI



Step 3. Deliver new In-Memory Applications (e.g. BPC, Demand Planning, etc.)

Step 4. Zero Latency Replication – Real-Real Time

Step 5. Eliminate All 3rd Party DBs – Run In-Memory

Any

Data

SAP

Non-SAP

Accelerated

Accelerated

Applications

FutureIn-Memory ECC


HANA 1.0 – Scenarios

1. SAP Environment 3. non-SAP Environment

HANA

SAP ECC

4.6c / 4.7 /

ECC 6.0

(or CRM,

SRM, SCM)

Data Replicator

or

ETL

EDW 1

EDW 2

EDW 2

ETL

2. Mix Scenario

BW

SAP Business Objects BI 4.0

In-memory

Business Scenario


HANA Certified HW Providers

Server Memory

Configuration

Data Volume(x5 compression)

# Rows(500 byte / row)

128 GB 320 GB 687 million

256 GB 640 GB 1.3 billion

512 GB 1280 GB 2.7 billion

1 TB 2.5 TB 5.5 billion

Clustered

Environment

(MPP)

unlimited unlimited

AGENDA







Telco - Customer Behavior Analytics

Telco challenges today are to measure and understand customer usage behaviors

• Focused marketing

• Conversion to post paid

• Increase prepaid spend

• Understanding customer behavior

• Fraud

• Real time data volumes

• Analysis speed

=


Analyze Prepaid Customer Behavior5

$

10

$

5$

3 days 5 days

Customer Id Topup spend Date

1 5$ 8 days ago

1 10$ 5 days ago

1 5$ Today

• Total Topup spend - 20$

• Total days - 8 days

• Average spend - 2.5$ / day


Analyze Prepaid Customer Behavior5

$

10

$

5$

3 days 5 days

1 day

2 days

2 days

3 days

• Detailed analysis of customer usage behavior patterns

• Focus marketing campaigns (Geography, Usage behavior etc..)

• Accelerate usage time = Customer spend money faster

• Reduce dead time = Customer always spend

• Link with dealer performance (Lower -> adjust incentives)


Analyze Prepaid Customer Behavior

Demo



Screen Shoots 1



Screen Shoots 2



Balance

(OLTP)

Topup history

(OLAP)

Mill

ion

Re

co

rds

Inse

rt \

Se

lect \

Upd

ate

Bill

ion

Re

co

rds

Inse

rt O

nly

\A

gg

reg

atio

n

Billing /

EDW

1. Read Current Balance

2. Calculate usage / dead time

3. Write To History

4. Update Balance


Why HANA For Customer Behavior Analytics

Column / Row ( OLAP / OLTP)

technology in same DB / Transactions

• Develop new type of scenarios

• Reduce solution complexity

• Low TCO

In-Memory • Fast Query

• Fast DB operations

• No pre aggregation

• Scalable

• Easily developed

Compression • Reduce TCO

• Small HW footprint


The Future - HANA Content + Applications

Strategic Workforce

Planning

Smart Grid Analytics

COPA case (includes financial line item reporting)

Inventory Movement

Billing Management

Smart Grid Analytics

SAP BW Powered by HANA

SAP BPC Powered by HANA

Order to Cash analysis

Work Force Planning

High Tech: Operational Reporting for Operations and Finance, Sales Pipeline Analyzer

Banking: Bank Analyzer

20+ SAP Projects - H2 2011 for Ramp-up & GA Early 2012


Customer Scenario Data Source Benefits

Chemicals

(DE)

profitability analysis ERP 8TB ERP 6.0 EhP4

on Oracle/AIX

query performance,

load time/latency

Manufacturing

(DE)

material disposition,

production line utilization

ERP 1TB

BW 2TB

ECC 6.0

on DB2/AIX

BW 7.0

on DB2/AIX

reduce extraction impact on

ERP,

query performance

Apparel

(US)

demand planning,

order planning

ERP 2TB R/3 4.6c on Oracle

BW 7.0

Teradata

load time/latency (currently 24h

with Teradata, target is 15 min)

CPG

(US)

multiple ABAP reports

(MB51 material document list,

FAGLL03 GL line item display, KE27

periodic valuation), CPG warehouse

scorecard, trade latest estimate,

customer service dashboard

ERP 10-200 mio.

records of relevant

data

ERP 6.0 EhP4

on Oracle/Linux

BW

query performance

Manufacturing

(CH)

customer contact listing,

open quotes/open orders

ERP

200 m. orders, 9 m.

contacts

ERP 6.0

on Oracle/Linux

impact on ERP,

query performance

SAP GFO

(DE)

sales pipeline analysis CRM 2,7TB CRM load time/latency, performance

CPG

(US)

profitability analysis ERP 6.0

on DB2 on AIX

query performance,

load time/latency

SAP HANA – Example Pilot Customers


SAP Applications using In-Memory Technology

Next wave of technology innovation

Combined in-memory analytics & transactional applications

Available today, delivered without disruption

Continuous real-time link between insight, foresight and action

Plan Smarter

…run faster …perform better

To Empower Your Organization…

…plan smarter


3 Steps for HANA Project

Step 1

Introduction to HANA

Duration: 2 hours

Agenda:

overview presentation about

HANA and SAP in-memory

strategy.

Target Audience:

• Line of business managers

• Reporting managers

• Solution architects

• Data warehouse team

Step 2

Scenario Workshop

Duration: 1-2 days

Agenda:

Identify potential HANA

scenarios within customer

landscape and provide an

overview on BI4.0

Target Audience:

• Line of business managers

• Reporting managers

• Solution architects

• Data warehouse team

Step 3

Scenario Development

Duration: 60 days

Agenda:

Develop potential scenario on

HANA and BI4.0


More information ...

Visit

http://www.sap.com/platform/in-memory-computing/index.epx

SAP Developer Network

http://www.sdn.sap.com/irj/sdn

Hasso Plattner Institute, Potsdam

http://www.hpi.uni-potsdam.de/













Questions?

Thank you!

Noam Berda: [email protected]

@noamberda

Noam Berda

mailto:[email protected]

http://twitter.com/

http://twitter.com/

http://sg.linkedin.com/pub/noam-berda/1/a10/276

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