What You Need to Know About SAP NetWeaver BW

Powered by SAP HANA

Daniel Rutschmann Director, BW on HANA at SAP America

February 16, 2012

BI Expertwww.BIexpertOnline.com

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Agenda• Introduction• SAP NetWeaver BW powered by HANA• Brief tour of BI Expert Web site• SAP NetWeaver BW powered by HANA (cont.)• Q&A

Adoption of SAP NetWeaver BW constantly growing

More than 13.000 customers referring to more than 15.000 productive systems

Stable Product, Large installed Base, Constant Growth

Productive SAP NetWeaver BW systems – constant growth

Q3 09

Q4 09

Q1 10

Q2 10

Q3 10

Q4 10

Q1 11

Q2 11

Q3 11

12,000

12,500

13,000

13,500

14,000

14,500

15,000

15,500

16,000

13,9

10

14,2

17

14,4

50

14,6

93

14,9

52

15,2

43

15,5

33

15,6

71

15,8

32

Reliable Data Acquisition

Reliable Data Acquisition

Business Content

Business Content

Streamlined Operations

Streamlined Operations

Lifecycle Management

Lifecycle Management

Fast, sustainable implementation: Modeling Patterns Business Content

Load any data with trust and quality: Out-of-the box integration for data originating in SAP systems Integrated with SAP BusinessObjects Data Services (Data Integrator and Data Quality Management)

Efficient, streamlined data management: Management of data consistency Sophisticated Security, Authorization and Identity Handling High availability

Sophisticated lifecycle management at different levels: System Meta Data Data (Nearline storage, archiving)

Integrated, scalable Enterprise Data Warehouse (EDW) platform

Existing SAP BW Reference customers

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

– DataStoreObjekt (DSO) (e.g. Activation)– Integrated Planning (e.g. Disaggregation)

• Missing capability of integrating different data mart types on a single platform

– Architected data marts vs. operational data marts vs. agile data marts

• 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)

Typical Bottle Necks - Short Comings of current Approach

ABAP AS Next Generation

Next Generation Apps

SAP HANA

Data inmemoryRuntime

Procedurecode

Programcode

compile& deploy

Fast datatransfer

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 BWSAP NetWeaver BW

Data ModelingData Modeling

HANA

Data ManagementData Management

Data StorageData Storage

Analytical / PlanningEngine

Analytical / PlanningEngine

DBMS

SAP NetWeaver BWSAP NetWeaver BW

Data ModelingData Modeling

Analytical / PlanningEngine

Analytical / PlanningEngine

Relational Database

Data ManagementData Management

Data StorageData Storage

SAP NetWeaver BW – Fully In-Memory Enabled EDW

BWA

SAP NetWeaver BW Powered by HANASupercharge BW Applications

SAP NetWeaver BW

RDBMS

SAP ERP

RDBMS

ETL

SAP HANA

Non SAP

Data sources

Primary Database for BWFoundation for new Applications In-Memory database used as primary persistence

for BW

BW manages the analytic metadata and the EDW data provisioning processes

Detailed operational data replicated from applications is the basis for all processes

SAP HANA 1.x will be able to provide the functionality of BWA

High-performance foundation for new SAP applications

SAP Applications

SAP BusinessObjects BI

Supercharge BW with Dramatically Improved Performance

Time becomes your competitive advantage With faster reporting and more insightful analysis With faster data loading and decreased data latency

Simplified Administration and Streamlined Landscape

Efficiency becomes your competitive advantage With reduced IT workloads and administrative tasks With a simplified system landscape and less data storage

Unlock The Power of Your Data Across The Enterprise

Business flexibility becomes your competitive advantage With access to ALL your data down to the most detailed level With business users empowered to quickly get the answers they need

Preserve Your BW Investment without Disruption

Innovation without disruption becomes your competitive advantage With no disruption to your BW application but with all the benefits of a supercharged system With minimal impact to business users and administrations in terms of training

A brief tour of the BI Expert Web site

SAP NetWeaver BW 7.x on xDB Standard DataStore Objects Data Base server and SAP BWA Standard InfoCubes BW Integrated Planning HANA Data Marts running side-by-side BW

SAP NetWeaver BW 7.3 on HANA HANA as a Platform In-Memory based InfoCubes In-Memory based DataStore Objects In-Memory planning engine Consumption of HANA artifacts created via HANA

studio BW staging from HANA

Migration without reimplementation - no disruption of existing scenarios

Access Data Faster

– Improved Reporting and query performance over traditional

RDBMS

– Query acceleration on BW DataStore Objects(DSO)

– Acceleration via In-Memory column storage

– Additional acceleration via Analytic Views on top of DSO

Real-Time Operational Data Access

– Transient InfoProider dynamically generated on top of Analytic

Views during Query runtime

Reduce Latency

– Performance boost for ETL processes

– Faster delta loading via massively improved load window

Accelerated reporting performanceSpeed

Scale

Flexible

HANA

SAP NW BWQuery on

InfoCube, MasterdataAnalytic Index, Composite

Provider

Query on InfoCube, Masterdata

Analytic Index, Composite Provider

Query on DSO, BW InfoSet

Query on DSO, BW InfoSet

SAP HANA SQL Engine Calc Engine

Aggregation Engine on In-Memory data

Classic BWStar Schema

HANA OptimizedStar Schema

SAP HANA optimized InfoCubes represent “flat” structures Up to 5 times faster data loads (lab results) Faster remodeling of structural changes After the upgrade to BW on HANA all InfoCubes remain unchanged Tool support for converting standard InfoCubes (lab result: 250 Million records in 4 minutes) No changes of processes, MultiProviders, Queries required

No dimension tables No second fact tables

HANA optimized DSOs• Delta calculation completely

integrated in HANA• Using in-memory optimized data

structures for faster access• No roundtrips to application server

needed• Speeding up data staging to DSOs

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 HANA optimized DSOs

• No changes of data flows required

DatabaseLayer

DatabaseLayer

User interfaceLayer

User interfaceLayer

ApplicationLayer

ApplicationLayer

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

HANA Optimized DSOs provide faster activation times!

Delta: 0.1 M, Active: 1 M Delta: 1 M, Active: 10 M Delta: 10 M, Active: 100 M0

500

1000

1500

2000

2500

3000

3500

4000

4500

20sec5 min

75 min

3sec41sec

8 min

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

Runti

me

in s

econ

ds

Push-Down Planning Logic to SAP HANA

– Traditional Planning runs planning functions in the

App. Server

– In-memory Planning runs planning functions in the

SAP HANA platform

Performance boost for planning capabilities

– Aggregation, Disaggregation

– Conversions, Revaluation

– Copy, Delete, Set value, Repost, FOX

– Performance boost for plan/actual analysis

Non-Disruptive

– No changes of planning models, planning

processes, MultiProvider, Queries required

In-Memory enabled planning for shorter planning cycles!

DatabaseLayer

DatabaseLayer

User interfaceLayer

User interfaceLayer

ApplicationLayer

ApplicationLayer

Presentation

Orchestration

Calculation

Data

Presentation

Orchestration

Calculation

Data

SAP NW BW

SAP NW BW SAP NW BW

SAP NW BW

SAP HANA xDB

Tight integration between HANA Data Mart scenarios and SAP NetWeaver BW Providing additional flexibility by combining

ad-hoc data models from Data Marts with consolidated data in the EDW

No need to manually create/maintain Metadata for Analytic Views in SAP NetWeaver BW

Transient InfoProider dynamically generated on top of Analytic Views during Query runtime

Query: e.g. Analysis, Xcelsius, Web Intelligence (WebI)

Integration BW Analysis Authorization Concept

SAP BW can leverage real-time operational data-marts from SAP HANA!

BW Schema Non-BW data

InfoCubeTransient Info

Provider

Composite Provider

Query Query

SAP BW

SAP HANA

Reporting & Analytics Tools

SAP HANA

any schema

BW application

BICS, MDX, SQL

ReplicationServices

Data Services

SAP Extractors

Data Services

HANA

BW managed schema

BW workspace

Explorer Universes (SQL) other SQL clients

Analysis (Pioneer) BICS Universes

(WebI, CR, XC)

BICS, MDX, SQL

Planned functionality, not yet available.

Scope tbd, butwill be limited.

Feedback from Early Customers

• 100x Faster Reporting

• 10x Faster Data Loading

• 3x faster than BWA!

• 20% Reduction in Admin & Maintenance FTE• Estimated by customer. Customer plans on re-deploying FTEs to BI projects

Selected Early Results– 1. Data Mart Process Performance – Before: 3 hours a day, for populating physical Data Marts within BW– After : 3 minutes for the same data volume, Data Marts are logical views only

– 2. Change Management Performance after structural changes (e.g. add/delete fields)– Before: ALTER TABLE (triggering data re-alignment) on ORCL and rebuilding BWA index taking 7+ hours– After : ALTER TABLE is just a “drop/add column” and BWA index is not required anymore taking < 1 min

– 3. Staging / Data load Performance – Before: data is loaded to DSO, delta is calculated (activation), delta is loaded to InfoCube – taking 9:08h (4:30h + 56

min + 3:20h)– After : same process, but using HANA-optimized object types and delta calculation within HANA – taking 3:20h (2h

+ 4 min + 1:14h)

– 4. Query Performance– Before: All queries’ performance is within good level, if BWA is attached – After : Current performance is at least the same as with BWA or better (with potential for further optimization)

Ramp Up Program SAP NetWeaver BW powered by HANA

Customer Industry Region Size of POC Scenario

Retail Customers EMEA, APJ, South America 800GB – 5 TB

POS-DM, Financial Reporting, Procurement Scenarios, Materials Management, Shelf Analysis, Detailed Stock

reporting, General Ledger, Material Assortment Planning

Consumer ProductsEMEA, APJ,

North&South Americas

320 GB – 5 TB Operational Reporting on ERP data (e.g. SD, MM), CRM scenarios, Shop reporting

Utilities, Public Sector EMEA, APJ, North America 1 – 2 TB

Reporting on ERP data, Fleet and Plant Management reporting, Customer Care, Billing, HR, Energy

Consumption Analysis and planning

Oil & Gas, Chemical EMEA, APJ 1 – 5 TB Classical ERP Reporting, revenue stream and chash-flow analysis,

Automotive EMEA, APJ 160GB – 5TB Classical ERP reporting, Material Movement, Financial Reporting and planning

PLUS more Ramp Up Customers in Telecommunications, Finance, Pharma, Healthcare, HighTech

www.BIexpertOnline.com

Questions?

To “raise your hand” online for a question, click the hand icon and

we will unmute you.

Resources■BW powered by HANA Online Help– http://help.sap.com/nw73bwhana■Experience SAP HANA– www.experiencesaphana.com■SAP HANA optimized planning–

http://www.sdn.sap.com/irj/scn/weblogs?blog=/pub/wlg/27521■Support for multiple applications on SAP HANA – Note 1661202

and 1666670■SAP BW on HANA Sizing – Note 1637145

Contact informationDaniel Rutschmann • Director, BW on HANA• Daniel.Rutschmann@sap.com

Scott Wallask• Managing Editor, BusinessObjects Expert• Email: scott.wallask@wispubs.com