SAP HANA Inside Track

SAP HANA Inside Track

David Hull, SAP

July 30, 2012 Public

SAP HANA Inside Track

David Hull

July 30, 2012

© 2012 SAP AG. All rights reserved. 2

Agenda

Time Session

1:00pm-1:10pm Welcome

1:10pm-1:45pm SAP BW on HANA

Rohit Kamath, SAP

1:45pm-2:30pm

Hadoop, SAP BI and HANA - Healthcare Industry Example

Jeff Krone from Zettaset will discuss how automating the Hadoop process using

Zettaset Orchestrator can deliver new levels of operational efficiency to the

healthcare industry, including faster patient on-boarding and tighter compliance with

new Affordable Care Act mandates.

2:30pm-3:15pm HANA in the Cloud - Options and Alternatives

Yusuf Bashir, SAP

3:30pm-4:15pm HANA & the SAP BI Landscape

Hari Guleria, Independent Consultant

4:15pm-5:00pm

Live HANA Demos: Advanced Text Search (with HTML5 UI); Smart Meter Analytics;

Business Objects Explorer running on 3TB HANA dataset

Chris Hallenbeck, SAP


Be Part of the Twitter Conversation

#sitpal #HANA

Thank you

BW on SAP HANA

Rohit Kamath

July 30, 2012


Disclaimer

This presentation outlines our general product direction and should not be relied on

in making a purchase decision. This presentation is not subject to your license

agreement or any other agreement with SAP. SAP has no obligation to pursue any

course of business outlined in this presentation or to develop or release any

functionality mentioned in this presentation. This 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 merchantability, fitness for a particular purpose, or non-

infringement. SAP assumes no responsibility for errors or omissions in this

document, except if such damages were caused by SAP intentionally or grossly

negligent.


Agenda

Introduction

Breaking new grounds for in-memory technology

Enterprise Data Warehousing with SAP BW

DWH Application and DB Platform in symbiosis

Typical bottle necks caused by the RDBMS paradigm and the two tier approach

SAP HANA In-memory Data Base

In-memory database technology for Dummies

SAP HANA 1.0 SPS3 – data base specific features

SAP BW on SAP HANA database: value proposition

Modeling and Dataflow Aspects for an HANA based BW

BW‟s Layered Scalable Architecture (LSA) in times of HANA

HANA optimized modeling objects in BW

Summary and Outlook

Six key points to take home

Appendix – Migration considerations

Enterprise Data Warehousing with SAP

DWH Application and DB Platform in symbiosis

Typical bottle necks caused by the RDBMS paradigm and the two tier

approach


Reliable

Data Acquisition

Business

Content

Streamlined

Operations

Lifecycle

Management

Fast, sustainable implementation through

Modeling Patterns

Business Content

Openness and data quality through

Out-of-the box integration for data originating in SAP systems

Integrated with SAP BusinessObjects Data Services (Data Integrator and Data Quality Management)

Efficient data management through:

Management of data consistency, data base abstraction, data base neutral

Sophisticated Security, Authorization and Identity Handling

High availability

Enable sophisticated lifecycle management at different levels:

System

Meta Data

Data (Nearline storage, archiving)

Integrated, scalable Enterprise Data Warehouse (EDW) platform

EDW = DBMS + BW

SAP NetWeaver Business Warehouse Strong EDW capabilities - Overview


SAP NetWeaver Business Warehouse EDW Model and Dataflow Definition

Define a central EDW model that satisfies

the need of decision makers across all

areas of a company and acts as a single

point of truth for any kind of information

Dataflow Modeler

Define ETL processes to populate the

persistency layers of the EDW Model with

cleansed and consolidated, consistent and

harmonized data in an adequate periodicity,

will say periodically based on batch or near-

real or real time processes

Transformations / DTP

Source System handling

Realtime Data Acquisition (RDA)

LS

A

Reporting Layer

Business Transformation Layer

Op

era

tiona

l Da

ta

Sto

re

Data Propagation Layer

Harmonisation Layer

Corporat

e

Memory

Data Acquisition Layer


SAP NetWeaver Business Warehouse Scheduling and Monitoring the Dataflow

Organize, schedule and monitor the

dataflow towards and within the EDW and

provide tools to repair or redo unexpected

failures during load processes.

External ETL Processes

Metadata Management

Process Chains

Admin Cockpit

generating Repair Chains

checking Error DTPs


Stagin

g

Acceleration Archiving

SAP NetWeaver Business Warehouse EDW Persistency and Performance Management

Provide Data Management capabilities in

order to massage the data persistency

according to the specific characteristics of the

data and information partitions such as actual,

frequently asked data, volatile data that is

going to be updated very likely, old, read only

data – with nearly no demand for reporting,

data that has to be hidden but kept for legal

reasons

Provide a technology for high performance

OLAP processing on top of all parts of the

data resulting out of adequate modeling

features (like Star Schema), particular

persistency layers in the model (granular vs.

aggregated data resp. information) and

sophisticated storage paradigms


Typical Bottle Necks - Short Comings of current Approach

Missing analytical capabilities on DB level lead to massive AppServer/DBServer traffic

– DataStoreObject (DSO) (e.g. Activation)

– Integrated Planning (e.g. Disaggregation)

Distributed data management (RDBMS vs. BWA vs. NLS vs. Archive)

– Missing data aging strategies in RDBMS

Nature of RDBMS - tupel based data storage, indexing necessary for performance

– Read/Load Performance on the RDBMS (e.g. Extended SAP Star Schema too complex)

Other Examples

– Exception Aggregation (e.g. Distinct Count only available as BWA Calculation Engine

feature)


SAP NetWeaver BW Accelerator 7.20

This 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 merchantability, fitness for a particular purpose, or non-infringement.

Enhanced built-in analytical capabilities*

F4-Value help

MultiProvider calculation handling

Exception aggregation (min, max, count distinct)

“BW Workspace” Analytic indexes

Advanced features*

BWA based InfoCube

Use DataStore Objects to create indexes

Addressing the RDBMS read and calculation performance bottleneck

SAP BW

Accelerator Calculation

Engine

Aggregation

Engine

Index

ABAP AS

App

RDBMS

Today

SAP HANA In-memory Database

In-memory database technology for Dummies

SAP HANA 1.0 SPS3 – data base specific features


In-Memory Computing – a new Lifestyle

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


In-Memory Computing – The Time is NOW Orchestrating Technology Innovations

HW Technology Innovations

64bit address space – 2TB in

current servers

100GB/s data throughput

Dramatic decline in

price/performance

Multi-Core Architecture (8 x 8core CPU

per blade)

Massive parallel scaling with many

blades

One blade ~$50.000 = 1 Enterprise

Class Server

Row and Column Store

Compression

Partitioning

No Aggregate Tables

Insert Only on Delta

The elements of in-memory computing are not new. However, dramatically improved hardware

economics and technology innovations in software have now made it possible for SAP to deliver on its

vision of the Real-Time Enterprise with in-memory business applications.

SAP SW Technology Innovations


Company

[CHAR50]

Region

[CHAR30]

Group

[CHAR5]

INTEL USA A

Siemens Europe B

Siemens Europe C

SAP Europe A

SAP Europe A

IBM USA A

0

1

1

2

2

3

0 INTEL

1 Siemens

2 SAP

3 IBM

1

0

0

0

0

1

0 Europe

1 USA

0

1

2

0

0

0

0 A

1 B

2 C

NewDB Column Store: Dictionary compressed

Classical DB

1 x „0“

2 x „1“

2 x „2“

0 INTEL

1 Siemens

2 SAP

3 IBM

0 Germany

1 USA

0 A

1 B

2 C

NewDB Column Store:

Run length compressed*

1 x „1“

4 x „0“

1 x „1“

1 x „3“

1 x „0“

1 x „1“

1 x „2“

3 x „0“

* Note that there is a variety of compression methods

and algorithms like run-length compression

(see Comparison of Compression Algorithms`) +

SAP HANA Database - Technology Multiple data storage methods: Column Store

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.62.7291&rep=rep1&type=pdf


Scale – SW side distribute across cores

Da

ta

Hot Standby Blades for Failover

Data Distribution

RAM locality – data gets spread out to all

available cores

MPP execution – blades share nothing when

crunching large data sets

Failover - Individual blades may fail without

causing problems


What is New in SAP HANA ? – Overview I

SAP HANA Database for SAP Business Warehouse

SAP HANA as database for SAP BW 7.30

Single In Memory Persistence and Storage - all BW tables are In-Memory objects

New In-memory optimized Data Store Objects (with SAP BW 7.3 SP5)

New In-Memory DSO activation process

New partitioning options

New In-memory optimized InfoCubes (with SAP BW 7.3 SP5)

Simplified design and indexing

Faster data loads and simplified modeling

In-Memory Planning engine

Based on existing Integrated Planning (BW-IP)

Push down of OLAP engine into SAP HANA from SAP BW‟s ABAP layer

Tools to seamlessly migrate BW underlying database to SAP HANA

Planning Engine

In Memory operations like Disaggregation, copy, write-back

Supporting BW – IP and Business ByDesign application use cases

Includes linear equation solver


What is new in SAP HANA SPS3? – Overview II

Data Acquisition

New HTTP/XML based data acquisition option with support for SAP

Application and SAP BW extractors

Further integration of ELT features in SAP HANA with Data Services

Back-up & Recovery and Security

Log backups and Point-in-time recovery

SSL connection encryption with certificates for client connections

SAP Identity Management (IDM) integration for user provisioning into SAP

HANA

Administration and monitoring

Integration into Solution Manager

Performance Warehouse

Alerting Infrastructure

DBA Cockpit

Enhanced tracing capabilities

Improved resource usage statistics

SAP BW on SAP HANA database: value

proposition


MOVE calculations into database

Only transfer RESULTS

AVOID Bottlenecks – Data Transfer

APPLICATION

LAYER

Calculation

DATABASE

LAYER

Calculation

Classical Approach

Future Approach


ABAP AS Next Generation

Next Generation Apps

SAP HANA

Data in

memory Runtime

Procedure

code

Program

code

compile

& deploy

Fast data

transfer

Application vs. Database Server - Technical Overview

Applications – Tight coupling between Application Server and SAP HANA

ABAP AS

App

RDBMS

Today

In-Memory empowered

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


SAP NetWeaver BW7.3 powered by SAP HANA – Added

Value

Accelerated Performance

Excellent query performance as proven with BWA

Accelerated In-Memory planning capabilities

Performance boost for load processes

Simplified administration and infrastructure

DB and BWA merging in one instance for lower TCO

Simplified administration via one set of admin tools e.g. for Data Recovery and High

Availability

Column based storage with highly compression rates and significantly less data to be

materialized

No special efforts to guarantee fast reporting on any DB object

Simplified data modeling and reduced materialized layers

Integrated and embedded flexibility for Datamarts

Speed

Scale

Flexible


SAP HANA Database - BWA Aspects Provide a BWA-like Query Performance directly on any data in the HANA

Database

Some BWA-features behave just “as before”

Snapshot Indexes for Virtual- and QueryProvider

Analytic Indexes & CompositeProvider

BW Workspaces

BW

Open SQL / SQL92 BW Analytics API

Query on

•InfoCube, Masterdata

•AnalyticIndex,

CompositeProvider

Query on

DSO, BW InfoSet

SAP HANA

SQL Engine Calc Engine

Aggregation Engine on In-Memory data

BWA like query performance

BWA index obsolete

BW hierarchies

TopN filter

Exception aggregation

Currency conversion

(more to come) …

BWA like performance

on standard DSO tables

„in-memory‟ DSO with

optimized activation

algorithm

Modeling and Dataflow Aspects for HANA

based BW

BW‟s Layered Scalable Architecture (LSA) in times of HANA

HANA optimized modeling objects in BW

In-Memory Planning

Consumption of HANA Models/Data in BW

BW Staging from Sources in HANA


The Layers of SAP„s Reference Architecture (LSA)

LS

A

Reporting Layer

Business Transformation Layer Opera

tional

Data

Sto

re

Data Propagation Layer

Harmonisation Layer

Corporat

e

Memory

Data Acquisition Layer

Reporting

Data sources

Reporting, analysis-ready

Near-realtime, operational-like

BI applications (Architected Data Mart Layer)

EDW Layer (Single Point of truth, reusable, granular, complete history)

source system like service level, comprehensive, complete, master the unknown, long term

Apply business logic

digestible,

ready to

consume,

integrated,

unified data

create harmonized view,

guarantee quality, plausibility

gate

Extractor inbox, 1:1

from extraction,

temporary


Corp.

Memory

ODS Data Mart

Data Warehouse

Business transform

End-user access / Presentation

Provide data

Data Acquisition

Harmonization

Data Propagation

Reporting

Main Service : Spot for apps/Delta to app/App recovery Transform : Enriched || General Business logic Content : Data source || Business domain specific History : Determined by rebuild requirements of apps Store : DSO(can be logical partitioned)

Main Service : Decouple, Fast load and distribute Transform : 1:1 Content : 1 data source, All fields History : 4 weeks Store : PSA, DSO-WO.

Main Service : Integrated, harmonized Transform : Harmonize quality assure (in flow|| lookup) Content : Defined fields History : Short or not at all || Long term Store : Info source || IO/DSO/Z-table

Main Service : Make data available for reporting & planning tools Transform : Application specific/(dis-)aggregate/lookup Content : Application specific History : Application specific Store : IC,DSO, Info Set, Virtual Provider, Multi Provider.

Unchanged Data Warehouse Architecture – Real World Example

Source 1 Source 2 Source 3 Source 4 Source 5

Pro

ject

Go

vern

an

ce

IT

Go

vern

an

ce


Evolving In-Memory Footprint in SAP BWiew

Planning Engine

Data Manager

InfoCubes

DataStore Objects

Analytic Engine

Data Persistency

and Runtime

Data

Modeling

En

terp

rise D

ata

Wa

reh

ouse a

nd

Da

ta M

art

Mo

de

ling

with

SA

P N

etW

ea

ve

r B

W

BWA instead

of

aggregates

filter +

aggregation

BWA-only

InfoCubes

BWA reporting

for DSOs

reporting +

activation for

DSOs in-memory

in-memory

planning engine

first calculation

scenarios in BWA

additional

calculations

in-memory

MultiProvider

handling and

flexible joins

BW 7.0

DB + BWA 7.0

BW 7.3

DB + BWA 7.2 BW 7.3 on HANA

Data Provisioning

In-Memory

optimized

InfoCubes

Consumption of

HANA models in

BW

HANA data for

BW Staging

Example

3

Example

4

Example

2

Example

1


In-Memory DSO for SAP BW Example I

Leverage HANA technology to implement In-Memory Optimized DSOs

with a reduced amount of physical storage

Accelerate data loads

Allow faster remodeling of structural changes

No adoption of processes, MultiProviders, or Queries required!

Or - to make it short - …

Leaner & faster propagation layer!!!


Activation process in ABAP

Generates heavy load on the database

Roundtrips to the applications server for delta

calculation

Today„s DataStore Object

DataStore Object

Query

Delta

Upload

Active Data Change Log

Activation

Queue

Parallel upload

DataStore Objects (DSOs) are

fundamental building blocks for a Data

Warehouse architecture

There are 4 operations on a DSO:

Upload (of new data)

Activation (Calculation of the

current image)

Querying (the current image)

Delta upload (for delta feeds)

In today's RDBMS-based

implementation, the activation and

querying operations are extremely

performance-critical.

These can be highly optimized in the

SAP HANA database


DataStore Objects in SAP NetWeaver BW 7.30 Creation of consistent delta information

Delta calculation performed on the

application server, too complex to

push it down to the DBMS as SQL /

Stored Procedure

Roundtrips to application server

needed for delta calculation

Activation algorithm creates heavy

load on the DBMS

Sorted Full Table Scan

Data

Packages

Lookup Calculate

Delta Update


DataStore Objects in SAP NetWeaver BW 7.30 Creation of consistent delta information

Delta calculation performed on the

application server, too complex to push it

down to the DBMS as SQL / Stored

Procedure

Roundtrips to application server needed

for delta calculation

Activation algorithm creates heavy load

on the DBMS

Sorted Full Table Scan

Data

Packages

Lookup Calculate

Delta Update


SAP BW - Data Store Objects Main Principles

5455 I 30

5455 I 20

5455 I +30

5455 I -30

5455 I +20

Former Load 5455 I 30

Actual Load 5455 I 20

BW DataStore Objects are threefold

Activation Table Active Table Change Log Table


In-Memory Optimized DataStore Objects Using In-Memory Computing Technology

Replaced ABAP modules for request

activation and rollback by HANA DB

implementation

No data processing in ABAP after

loading a request into the activation

queue

Using in-memory optimized data

structures for faster access

No roundtrips to application server

needed

Optimization is transparent for the

user

HANA DB

Implementation


In-Memory Optimized DataStore Objects Overview and Design

Delta calculation completely

integrated in InMemDB – no data

processing in ABAP




needed

History Index

(column based)

Activation triggered by BW, performed

by InMemDB

Main Index

(column based) Delta Index

(column based)

View View


In-Memory Optimized DataStore Objects Mapping Between Application Server and HANA DB

Column based table

Calculation

View

Standard column based table

no primary key, performance

advantage 20%

Uniqueness checked by SQL

statement (DBMS exit)

Temporal table

Table replaced by

calc view (uses

history index to

create a change log

view of the data)

View calculates

technical key on the

fly

Multiple updates for

a particular key are

consolidated into

one

Before Image 5455 I -30

After Image 5455 I 20

Former Load 5455 I 30

Actual Load 5455 I 20

History Index Valid from

Valid to

......5455 I

30.........dt1..............dt2... Main Index

......5455 I 20 ..... valid from

dt2

Delta Index


In-Memory Optimized DataStore Objects Performance Figures

20

300

4500

3 41

473

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Delta: 0.1 M, Active: 1 M Delta: 1 M, Active: 10 M Delta: 10 M, Active: 100 M

Activation Runtime - Lab Results

BW 7.30 - RDMBS based In-Memory optimized

Using in-memory computing

technology

… one of the most time consuming

staging operations – the request

activation – was speed up

tremendously by factor 5 - 10

... storage of redundant data was

prevented

Runtim

e in

seconds


Summary: In-Memory Optimized DataStore Objects Accelerated data loads

In-Memory optimized DSOs

Delta calculation completely

integrated in HANA




needed

Speeding up data activation by

factor 5 – 10

Avoids storage of redundant data

After the upgrade to BW on HANA

all DSOs remain unchanged

Tool support for converting

standard DSOs into IN-Memory

DSOs planned

– No changes of Dataflows required

Database

Layer

Database

Layer

User interface

Layer User interface

Layer

Application

Layer Application

Layer

Presentation

DSO

Objects

Activation

Data

Presentation

DSO

Objects

Activation

Data

SAP NW BW

SAP NW BW SAP NW

BW

SAP NW

BW

SAP

HANA xDB

This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This 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, ei ther express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.


In-Memory InfoCubes for SAP BW Example II

Leverage HANA technology to implement In-Memory Optimized InfoCubes

with “flat” structures and no Dimension tables and E tables in order to

Accelerate data loads

Simplify Data Modeling

Allow faster remodeling of structural changes

No adoption of processes, MultiProvider, Queries required


Leaner & faster reporting layer!!!


HANA optimized InfoCube Design in BW

Physical schema of BW InfoCube

tailored

towards traditional RDBMS

Benefits:

Fast data loads (no DIMIDs) up to 80% time reduction

Dimensions not physically present simpler modeling and faster structural changes

All processes, all Queries and MultiProviders can remain unchanged

HANA can work with “flat” structures

and doesn‟t need E- and F-fact tables!

Facts

D

D

MD MD

MD MD

Migration / New Facts

M

D

M

D

M

D

M

D

F F E


Standard InfoCube with Migration Option

Facts

D

D

MD MD

MD MD

F E


In-Memory Optimized InfoCube

Facts

M

D

M

D

M

D

M

D

F


BW Integrated Planning – based on SAP In-Memory

Computing Example III

Leverage SAP HANA Database Technology to

bring data intense operations to the data

optimize disaggregation features in Integrated Planning


Smooth and fast planning tools …


In-Memory Database Classic Database

In-Memory Planning The Technological Change

Database

Layer

Application

Layer

User interface

Layer Presentation

Orchestration

Calculation

Data

Presentation

Orchestration

Calculation

Data

Authorization

Locking

Hierarchies

input enablement

list based planning

planning functions

MP handling

Conversions

aggregation


SAP NetWeaver BW 7.30 In-Memory Planning - Simple Disaggregation Example

Traditional Approach

1. Determine the delta +50

2. Disaggregate (in appl. server)

per week (52)

per branch (500)

26000 combinations / values

3. Send 26000 values to DB to save

HANA-Based Approach

1. Determine the delta +50

2. Send 1 value to DB

+ instruction to disaggregate and

how

3. Disaggregate (in DB engine)

per week (52)

per branch (500)

create + save 26000 values

user changes

a plan value


Consumption of HANA Models/Data in BW Example IV

Leverage BW infrastructure to report on models created in HANA

OLAP Engine to access HANA data

BW Metadata Repository reflects HANA artifacts

BW client support all kind of data within the HANA database

Integration to BW InfoProvider (via CompositeProvider/Workspaces)

Support Authorization Concept for meta-/data access


Smooth and simple integration, “no” modeling


Consumption of HANA Models - Overview Mixed Scenarios BW&HANA Schemas

BW Schema

HANA Schema(s)

HAN

A

BW

InfoCube

AnalyticView

Transient

Provider

Query

CompositeProvider

Query


Publishing HANA models Select Analytical View and generate VirtualProvider


Analysis for Microsoft Excel - I Analytical Indices/TransientProviders visible as DataSources


Analysis for Microsoft Excel - Il Query Result Example in Spread Sheet with Navigation Pane

Summary and Outlook

Six key points to take home


Six key points to take home Start spreading the news

The evolution of in-memory technology at SAP moves on

Latest stage: SAP HANA Database as a full fledged in-memory database

SAP BW as one of the first applications fully enabled to leverage the key strength of the new

HANA In-memory database

– Accelerated performance

o No special efforts to guarantee fast BWA like reporting on any DB object

o Accelerated In-Memory planning capabilities

o Performance boost for ETL processes

(DSO Activation 5-10 times faster, InfoCube load 5 times faster )

– Simplified administration and infrastructure

o DB and BWA merging in one instance for lower TCO

o Column based storage with highly compression rates and significantly less data to be

materialized and managed

o Simplified data modeling and reduced materialized layers

Dedicated optimizations available for different BW modeling objects

LSA reference architecture will stay as the recommended model in BW with slight changes

Thank you

Contact information:

Rohit Kamath

[email protected]

Hadoop, SAP BI, and HANA Jeffrey Krone,

Zettaset

June 30, 2012


What we will Cover (Agenda)

Why Hadoop

What Customers are expecting

Best Practice for Integrating Hadoop with SAP BI

Healthcare Business case

Take Away


Big data makes organizations smarter and more productive by

enabling people to harness diverse data types previously

unavailable, and to find previously unseen opportunities

April 17, 2012

Gartner

Why Hadoop?

http://www.gartner.com/it/page.jsp?id=1986015


Are companies really using Hadoop?

Source : SAP America

Backup Disaster

Recovery Monitoring Failover Continuation Requirements

Compliance Management Scheduling Alerts Installation Automation

Provisioning Security Configuration Access

Control Scalability Utility

Managing Big Data is a Complex Task

Analytics, BI

Core No-SQL

Hadoop

Distribution

Services

Management

Gap

S

Security

Integrate

Into

SAP BI

- Aberdeen Group

“80% of Fortune 500 companies have a Hadoop cluster, less then 20% have it in production”


Mask complexities of Hadoop with an enterprise-

consumable product to manage big data

Eliminates dependencies on professional services,

reduces IT resource requirements, and dramatically

lowers TCO

Single vendor capable of integrating Hadoop on

non-commodity hardware such as SSD, flash,

supercomputers, etc.

The Zettaset Platform

• meets all above expectations

• is easy to deploy, resilient, highly scalable,

flexible

• offers significant cost savings compared to other

Big Data Platforms.

What Customers are expecting


October 2012 compliance requirement

Fortune 500 Health Care Company

Requires detailed Physician‟s Billing Analytics

Current data in10‟s of TBs and is growing very fast with a high degree of inclusion of semi-structured and unstructured data.

Currently, the Health Care company is utilizing two legacy databases:

Legacy database 1: is utilized for importing the initial data, scrubbing it, and transferring the cleaned up data to the 2nd legacy database.

Legacy database 2: is utilized as the backend for Business Objects. In addition, the database enforces the business rules and transforms the data into the appropriate format for the BO Reports.

Business Case - Background

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CFwQFjAB&url=http://www.entnet.org/AboutUs/loader.cfm?csModule=security/getfile&pageid=137874&ei=PacRUNjPJILOiwL65IFg&usg=AFQjCNEbCDDLam8XFEEwVR15uO3mlteXig


Performance issues with Legacy Databases

Report generation, business rules processing, etc.

Scalability Issues

The Health Care provider has reached the capacity limit of their current system‟s technical capabilities. Thereby making it difficult to onboard new customers efficiently.

Database Schema updates are burdensome

Each minor change to one legacy database requires multiple manual table updates to both legacy databases.

No Automated Failover or Backup mechanism

Inability to import and analyze Unstructured Data

Technology Issues


Customer wants to prepare for the new health care “Compliance Mandates”

Evidence-based medicine in health care reform - NCBI

Obama plan for health reform includes evidence-based care – Healthcare IT News

The Implications of the Health Care Reform – James Brown, MD.

Expand analytical capacity and performance without compromise.

Broaden current product portfolio to their customer base.

Derive insight into new markets based on short term business intelligence and long-term big data analytics.

Ability to store, process, and analyze structured, semi-structured and unstructured data.

Business Drivers for Conversion

http://www.ncbi.nlm.nih.gov/pubmed/21860057




http://www.healthcareitnews.com/news/obama-plan-health-reform-includes-evidence-based-care




http://www.sbisvcs.com/Brochures/TheImplicationsofHealthCareReform.ppt


Business Expectations

Technology Designing

Business designing

Data Integration

Business Rules

Information Harmonization

Best Practice steps – for a Hadoop Integration

Business Needs

Expectations

Compliance

Data sources

Data Definitions

Data Source

Types

Data Owners

Data Volumes

Data Reduction

Master data

Data Quality

KPI definitions

Business Rules

Transformations

Harmonization

Security

Data Types

Extraction

Transformation

Reduction

Loading

Integration

HANA

Model Business

Rules

Business

Transformations

Business

Definitions

Harmonize for

Enterprise

Analytics

Structured

Semi-structured

Unstructured


HADOOP (ZTS ORCHESTRATOR)

Accommodate both structured and un-structured data

Pre-process and load the structured billing data via Hadoop

Combine structured and un-structured data within ZTS

Orchestrator and transfer it to SAP HANA via a Hadoop / HANA

Connector (SAP Provided)

Leverage ZTS Orchestrator as a long term data repository and

aggregator of all types of data.

SAP HANA / Hadoop Integration


SAP HANA / Hadoop Integration - 1


SAP HANA

SAP HANA enforces the business rules via stored procedures and its columnar

database utilizing their in-memory capabilities.

SAP HANA enables the Health Care Company to take a deep dive and perform

sophisticated analytics on their data providing their customers new insights into their

data.

SAP HANA / Business Objects enables real time reporting and analysis for their

customers.

SAP HANA is utilized for the 1) Enforcement of the business rules, 2) Analytics, and

3) For generating Business Objects reports.




Source: SAP America


SAP HANA New Reports / Analysis

Analyze Customer Billing Behavior – predictive analysis related to

forthcoming billings based on historical trends

Analyze physician ratings and determine how they correlate to patient

treatment and revenue.

Derive optimal treatments for patients based on doctor notations (i.e.

analyze treatment by doctors to resolve specific issues for Good Patient

Practices)

Streamline Customer‟s billing process and identify inefficiencies by

analyzing unstructured notes related to billing / insurance transactions for

patients.

Utilize SAP HANA to analyze unstructured text (i.e. patient notes, billing

notes) and derive actionable intelligence.



Demo of Sample Healthcare Analytics

DEMO


Business Reports (SAP HANA)


Business Reports (SAP HANA)


The (SAP HANA / ZTS) system enables Health Care

Customers to:

Accommodate both structured and unstructured data via the ZTS Orchestrator.

Combine Structured and Unstructured data within ZTS and transfer it to SAP

HANA to handle enforcement of business rules, transformations and

Unstructured Text Analytics.

Utilize the HANA In-memory capabilities and breadth of SAP Analytic

applications to perform sophisticated analytics (e.g. unstructured text analysis)

providing Health Care Customers with new capabilities of performance and

decision management.

Substantially enhance performance, scalability, and ability to perform true-real-

time reporting and analysis for Customers.

Serve as the long term Historical Data Store (ZTS)

Take Away


[

Your Turn


Datasheets:

Updated datasheet with specs, including Shadoop

Shadoop datasheet

Intel:

A Distributed Parallelized Platform for Handling Large Data” – Jeffrey Krone

Market Watch:

SAP Continues to Expand Capabilities and Scale of SAP HANA® Platform and Ease

Developer Adoption”

Zions Bancorporation articles:

CIO.com - Bank Adopts Security Data Warehouse to Fight Persistent Security Threats

Banktech.com - Banks Push Hadoop Envelope to Open Big Data's Secrets

Additional Resources

http://zettaset.com/info-center/datasheets/Zettaset Datasheet_V6_Orchestrator.pdf

http://zettaset.com/info-center/datasheets/Zettaset Datasheet_V6_Orchestrator.pdf

http://zettaset.com/info-center/datasheets/Zettaset_Datasheet_Shadoop.pdf



http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CFIQFjAC&url=http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/cloud-computing-journey-to-cloud-v2i1-paper.pdf&ei=tfYSUJjdFcvaigKurIGgBQ&usg=AFQjCNEQydgXSrIT98I2Khcg3El68Xsm4A

http://www.marketwatch.com/story/sap-continues-to-expand-capabilities-and-scale-of-sap-hana-platform-and-ease-developer-adoption-2012-05-16

http://www.marketwatch.com/story/sap-continues-to-expand-capabilities-and-scale-of-sap-hana-platform-and-ease-developer-adoption-2012-05-16

http://www.cio.com/article/692490/Bank_Adopts_Security_Data_Warehouse_to_Fight_Persistent_Security_Threats?taxonomyId=3089




http://www.banktech.com/business-intelligence/232600226








Zions Bancorporation articles:

CIO.com - Bank Adopts Security Data Warehouse to Fight Persistent

Security Threats

Banktech.com - Banks Push Hadoop Envelope to Open Big Data's Secrets

Shadoop:

CIO.com - Zettaset to Offer Role-Based Access Control for Hadoop













http://www.cio.com/article/700687/Zettaset_to_Offer_Role_Based_Access_Control_for_Hadoop







SAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet®, PartnerEdge, BW,

BusinessObjects, BO Explorer, HANA and other SAP products and services

mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP AG in Germany and in several other countries all over the

world.

Zettaset is the logo and trademark of Zettaset in USA and several other countries

All other product and service names mentioned are the trademarks of their

respective companies.

SIMH is respective logo of SAP In memory HANA Group in LinkedIn

No part of this presentation may be copied or reproduced without the total

presentation without written permission of the presenter or Zettaset

Disclaimer


Sponsors

Logo Name

SAP America

ZettaSet

K2 Partners

ICM America

http://www.experiencehana.com/

http://www.zettaset.com/

http://www.k2partners.com/

http://www.icmamerica.com/





SAP HANA in Cloud-Based Scenarios

Yusuf Bashir, HANA Solutions Management

July 2012

HANA in Cloud-Based Scenarios

Yusuf Bashir, HANA Solutions Management

July 30, 2012


Cloud Provides Unique Advantages

• Radical reduction in CAPEX

• No servers to buy

• Pay-as-you-go monthly subscriptions (OpEx)

• Elastic storage

• Increasingly unpredictable data volumes (e.g. Big Data)

• Data growth outpacing ability to persist locally

• Immediate provisioning & availability

• Potential for higher SLAs vs. internal IT capabilities

• Outsourced server administration and management

“Enterprise data will increase 650% over the next 5 years” –Gartner

“Enterprise data will double every 18 months” --IDC

By 2014, 31% of net new IT

spending will be invested in the

public Cloud

Spending on public Cloud services is

growing 6x faster than IT spending

generally

By 2014, the total market for public

Cloud services will be $56B, up

from just $17B in 2009


Cloud Landscape Is Crowded

Developers Prefer Options Around PaaS & IaaS


HANA High Performance

Cloud

• HANA Dev Edition Sandbox (free

30-day trial)

• HANA Dev Edition on Amazon

Web Services

• HANA HPC for Productive Usage

with select partners (coming)

SAP HANA for Cloud

Solution Areas

HANA “AppCloud”

• Sales & Operations Planning

• BI On Demand

• Expense Insight

• Successfactors Analytics

• Consumer Apps (e.g. Recalls+,

Charitra)

HANA Hosting

• Hosting & Managed Services

• Outsourcing & BPO/ITO

• Private Hosted Cloud

HANA for Cloud

3 1 2

SAP Cloud Apps

powered by

HANA HANA Hosting

HANA High-

Performance

Cloud


HANA “AppCloud”

Internal Use of HANA in the SAP Cloud

• Sales & Operations Planning (S&OP)

• BI on Demand Advanced Edition

• Expense Insight

• Successfactors Analytics

• Supplier InfoNet

• Consumer Apps (Recalls+, Charitra)

COMING SOON

NEW

COMING SOON


Hosting SAP HANA High Performance Cloud (HPC)

Benefits of Participating

Phase 1

HANA Hosting

Global network of most strategic HANA hosting

partners (by invitation only)

Same benefits as HANA Hosting, plus:

1. HANA Dev Edition to drive developer

adoption on paid cloud instances.

2. Ability to resell select certified HANA Apps

with run-time HANA license.

HANA hosting on certified HW

(open to all)

Benefits:

1. Hosting packages promoted to

SAP account teams.

2. Ability to resell HANA with

perpetual licenses only.

Phase 2

HANA High Performance Cloud

Internal deployment of HANA on cloud recommended as 1st step.

Visy (Telstra)

Komatsu (Telstra)

University of Kentucky (Dell)

Customers Using HANA Hosting Today Global HPC Partners Shortlisted:


SAP HANA High Performance Cloud

Entry Point for HANA Developers & ISVs



Benefits of Utility Pricing

Annual Costs Example

Co-Located Data Center With Utility Pricing

Server

Hardware $49,005

Instance

Hours $33,415

Network

Hardware $9,801

Data

Transfer $1,215

Hardware

Maintenance $17,642

Co-Location

Expense $504,187

Remote

Hands

Support

$6,075

Data Transfer $2,686

Total $589,395 Total $35,061

Capacity wasted with traditional on-premise deployments

HANA on cloud can help maximize capacity during peak use



Ideal Choice for Analytics on Big Data

Twitter generates over 300 Million Tweets

per day, translates to ~200TB of tweet data

per year.

Twitter‟s FireHose API delivers Tweets in

real time @ 260Mbps.

Assuming 0.1% relevancy of all Tweets to a

customer, 200 GB of tweets (“hot data”) are

loaded into HANA using a high speed

HANA Data Loader based on Data

Services

Millions of product, customer or supplier

master records can be pushed in real-time

from on-premise SAP ECC to HANA Cloud

using SAP Landscape Transformation

(SLT). This table can then be used as

filtering criteria to select the hot data to load

into HANA.

HANA 200 GB of Tweets & 3 million

product master records

HILO-based User Interface

Product

Master

HANA HPC

ON PREMISE

CLOUD

SAP Business

Suite

HANA Data Loader

Hadoop-based Store

200 TB Tweets/Year

~3M records from ERP

Data from other

Cloud

Applications via

Data Services

Twitter Firehose API

SAP or Partner App


SAP HANA License Models for Cloud

What‟s Available Today

* only available to SAP Partners.

1. Test & Demo*

Rental for 5K € per instance per year (tiered pricing + regional uplifts)

Perpetual for 15K € per instance + annual maintenance (tiered pricing + regional uplifts)

Classic hosting offered by cloud partners on certified platforms.

2. Development (Supported Platforms – Productive Usage)*

Perpetual for 2K € per user + annual maintenance (tiered pricing + regional uplifts)


3. Development (Non-Supported Platforms – Non-Productive Usage)

License at no cost, developers pays for instances

Offered by Amazon Web Services, non-supported platform.

4. Full Use Perpetual

Perpetual for 128K € per 64GB (HANA unit) + annual maintenance (tiered pricing + regional uplifts) or HANA

Edge for 40K € limited to 64GB.


5. COMING SOON: HANA App Run-Time*

Usage or flat royalty-based OEM restricted to select HANA Apps. Price of app determined by developer.

Only available with select certified HANA Apps through HANA High Performance Cloud partners.


SAP HANA for Cloud

Final Summary

HANA Cloud is a new deployment option (not a product)

Customers can deploy HANA in the cloud via hosting partners + benefit from OpEx

OEM of HANA for select certified apps coming with HANA HPC

Thank you

For more information please contact:

Yusuf Bashir

HANA in Cloud Solutions Management (Palo Alto Bldg 2)

[email protected]

+1 (415) 990-1333

mailto:[email protected]


Logically Organized Business Awareness

Executive Stakeholders: Dashboards and Info-widgets

Business Analysts: Self Service for „Don‟t Know‟ analytics

Management: Performance and actionable Decision Analytics

Operational: Data Reports and Graphical „Daily Reports‟

Right Information when you need it

Real-Time Benchmarking and Alerts

Information Consumption Workflow

How do I need my information access organized

What is BI all about


Globalize and mobilize Information

„One Company, One Truth‟ globally

Ease of access and use

Easy to access and find information in a logical Info Workflow

Self-Service Analytics

Increased decision output without increasing headcount

Performance Measures and Alerts

Management needs actionable KPI‟s for rapid response

Exception reporting

Proactive alerts management on business patterns & behaviors

Day-to-day global reporting

Instant access to current state of business status

Information Consumption Priorities


Why HANA

Application Type Records Query Run

Time

BW ETL delayed 20-50 mill < 10 seconds

BW Accelerator ETL delayed 60-300 mill < 10 seconds

Accelerated BO Explorer ETL delayed 60-300

million

<10 seconds

Accelerated BO WebI

Analytics

ETL delayed 60-300

million

< 10 seconds

HANA True Real-Time 1-‟N‟ billion < 5 seconds

•These are independent benchmark results

• BW results are dependent on optimal Architecture &

Modeling and clean-up of Cubes

• BWA Results are dependent on BW

• BO Explorer results are based on accelerated data

from BWA

• HANA results are based on optimized db transforms

and right-modeling


„Without business in business intelligence, BI is Dead‟

Make your Customer the new lead of the company

Gartner 2010


What we will Cover

HANA in a SAP BI Landscape

HANA Deployment Options

HANA Best Practice Methodology

Building your HANA SWOT Team

The Evolution Path of HANA

Social Network Questions


HANA Architecture

HANA Engine

Administration Reporting

Data Sources


HANA in the SAP BI Landscape

BOBJ

WebI

Crystal

Dashboards

Widgets

AD-HOC & PIXILATED ANALYTICS

Excel

Access

OLAP

BOBJ

Excel

Access

OLAP

BOBJ

Teradata

Oracle DW

LARGE Db‟s

DATA WAREHOUSE

SAP ECC SAP

SUITES NON SAP

SAP BW*

BEx

WAD

EP

DATA WAREHOUSE

SOURCE SYSTEMS

B

W

A

Accelerate

only BW

Queries

Search

Queries

E

X

P

L

O

R

E

R

Auto

Search

Data

SAP HANA 1.0

REAL TIME ANALYTICS Asynchronous

Operational

Data-loads

* BW ON HANA


The HANA Deployment Options

Stand Alone HANA Appliance

• SAP ECC Data Only

• Non-SAP Data inclusive

BW on HANA (All)

• Upgrade current BW to HANA

• „Big-Bang‟ approach – All or None

BW On HANA (Selective Passage)

• Upgrade select BW InfoProviders to HANA

• Selective BW Objects HANA deployment

New BW on HANA installation

• Each new BW Object designed for HANA from the start

• Database level transforms from the start

HANA Platform (Net New)

• ECC on HANA

• SAP BI on HANA


HANA Best Practice Flow H

AN

A S

I


The HANA Best Practice Flow

Customer SI & SAP HW Partner

Build Business Vision Business Value Attainment NA

Introduce to RDS‟s SI & SAP NA

Run HANA POC Choose RDS and run demo NA

Work with Business Identify true business value NA

Business Expectations Identify business Expectations NA

Run Pilot Load Customer databases NA

Confirm Vision Confirm vision Sizing

Place HANA Order NA Delivery 8-12 wks

Start building HANA On SI or SAP HANA appliance WIP

HANA arrives Update Patches and check HW Checks

HANA Start Migrate all HANA to Customers Box Checks

Save 8-12 weeks Continue development NA


The HANA Best Practice Checklist

Follow scientific

methodologies and

processes

Plan your work and only

then work your Plan

Follow the scientific rules

of BI & HANA deployment

Leverage

the HANA

checklist


• Identify the HANA - SWAT Roles

• Identify the skills required for each Role

• Measure each resource against all the skills

• Finalize what additional training is required

to match role to skills

Building the HANA SWOT Team


Stand-Alone BI Appliance

BW on HANA

The SAP HANA Platform

ECC on HANA

ECC and BW running on a common HANA db

The HANA Evolution

Stand Alone HANA

BW

BW on HANA

BW ECC

ECC & BW

using same HANA

Future Current

BW

ECC & BW on

separate HANA

ECC


From HANA projects and Social Network discussions

HANA Questions


Today there are more certified consultants than HANA projects

(current estimate is around 10k HANA certified resources)

But this is going to change quite fast

Green-field HANA projects mandate SAP consultants

Within a few months customers are starting to do it on their own

If SAP continues on their projected path we will need around 20 to 30k consultants

in the coming years

One of the greatest risks to SAP HANA is a lack of qualified HANA resources with

real customer experience. Not just technical resources but resources with the

customer experience, capabilities to align business vision and a passion for true

‘Business Value Attainment’

Now that I am certified what next?


As HANA is a database it can replace Oracle or DB2 but not the DW

If we look a little closer BW is actually becoming more critical, and stronger,

in the SAP BI landscape

BW on HANA

No direct ECC extracts to WebI, unless via BW

WebI, Crystal & Dashboard now talk directly to BW without a universe

In ECC 7.3, SP 3 we have a hidden BW in ECC for pushing data to HANA stand-

alone appliances

Will HANA Replace BW?


Stand Alone or BW on HANA

Very difficult to forecast the future, but am willing to bet (based on what is

happening in Europe)

BW on HANA will win the strategic race..

Many US customers start with a stand-alone HANA

Most are starting to evolve to BW on HANA

ABAP for HANA is already on the way

There are over 17,000 BW installations worldwide

Each one of these is a potential BW on HANA customer

In the long-run what HANA type will win


Customers don‟t need just a HANA installed (technocratic installation)

They need

BVA ( Business Value Attainment)

Information that enables them to make better decisions

Review data without constraints of data volumes

Look at their business in true real-time – globally

Look at business from inside, value chain and the outside

What do Customers really need?


1 year ago it was difficult to answer this question, today we can say

with confidence that HANA is a stable platform

Current reports confirm over 400 HANA licenses issued, with around

150-200 implementations underway

The target of 200 million revenue was crossed, the 2012 target is 400

million and right now is on track

Europe has more BW on HANA initiatives, and the US has more

Stand-Alone HANA implementations

HANA is the fastest SW launch in the history of SW launches

How Stable is HANA today?


Need extractors to work Faster

This is not happening currently, not a technology capability issue - but process and

knowledge issue

Real skills don‟t come from training, but from customer experience

Right now this is not happening at a large enough scale

We are all caught in the license vs. BVA sales conflict

At the „tipping Point‟ will there be enough great resources to carry the torch

of business excellence

Pushing customers too hard on a sales and less on true vision and business

value

What are the current Risks?


1. Build surgical Applications (Partners & Startups)

a. App Types

i. General apps that most can use

ii. Commoditized Apps by Industry and segment

iii. Competitive differentiator Apps

b. SAP needs to support Partners and Start-ups a little more

2. A solid Business Focused Methodology

a. Build a culture of „Business Excellence‟ and not simply of deploying the

technology

3. Define a true HANA vision prior to commencing

a. Just a TCO goal is not enough- there has to be more

b. Run a Vision session with business prior to planning

c. Use RDS‟s for initial POC and Pilots where possible

What are the sweet spots in HANA?


We need to compare apples to apples

SAP is very transparent on the HANA pricing. It is a single price based on per

GB utilized

Costs can be planned and controlled

Costs can be lowered by eliminating InfoElements® in BW environments and not

carrying junk InfoObjects into HANA (works for BWA too) = Minimum data for

maximum information

Never forget the cost of moving a single element from your source system

into your BI environment with ECC intelligence.

Exalytics & Teradata have a suite of products and applications with multiple

products, complex pricing and licensing. Hidden fees make strategic TCO

difficult to predict and far higher

When dealing with very large data volumes in true-real-time - there is no

competition for HANA

Is HANA really very costly?


Cost consideration or Competitive positioning

Size From What they do

Medidata $200 million US Clinical Trials & Service for Pharma

NRI NA Japan Data Processing services

Hilti $1.9 billion Liechtenstein Construction tools and products

Adobe $7.8 billion US Desktop Publishing SW

B/S/H $9 billion German Home Appliances

Surgutneftegas $12 billion Russia Oil & Gas

Colgate Palmolive $15.6 billion US Home & Hygiene cleaning products

Lenovo $16.7 billion Hong Kong PC manufacturer

Centrica $34.7 billion UK Integrated Energy services

Proctor & Gamble $78 billion US Home products

BASF $ 84.7 billion German Chemicals

…or how big do I need to be to consider HANA


Your Turn

[


www.experiencesaphana.com

SAP site for all HANA related information

Rapid Deployment Solutions

http://bivaluenomics.blogspot.com/

My Blog for SAP BI and HANA

Linkedin „SAP In-Memory HANA‟ Group

Worlds largest HANA Social Group

BI Valuenomics – The story of meeting business expectations in BI

Book published in 2010

Comparative Analysis

Comparison between HANA, Teradata, Exadata and Exalytics

The BI Eye-Q Test


http://www.experiencesaphana.com/

http://bivaluenomics.blogspot.com/

http://www.linkedin.com/groups?gid=3683024&trk=myg_ugrp_ovr



http://bookstore.authorhouse.com/Products/SKU-000366533/BI-Valuenomics.aspx

https://www.box.net/s/diybx201sryg3md4vhzg






SAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet®, PartnerEdge, BW,

BusinessObjects, BO Explorer, HANA and other SAP products and services

mentioned herein as well as their respective logos are trademarks or registered

trademarks of SAP AG in Germany and in several other countries all over the world.

BIDT is the logo and trademark of BI Databridge in USA and several other countries

All other product and service names mentioned are the trademarks of their

respective companies.

SIMH is respective logo of SAP In memory HANA Group in LinkedIn

No part of this presentation may be copied or reproduced without the total

presentation without written permission of the presenter or BI Databridge llc

Disclaimer


SPONSORS

Logo Name

SAP America

ZettaSet

K2 Partners

ICM America







