SAP Innovation Summer Camp Come with a Curiosity – …fm.sap.com/data/UPLOAD/files/08 SAP_Innovation... · SAP Innovation Summer Camp Come with a ... DataStore Objects in SAP NetWeaver

SAP Innovation Summer Camp

Come with a Curiosity – Leave with a Plan

SAP BW 7.30 on HANA

Track Hosts:

Lars Buescher & Gilberto Henn

SAP AGS, July 2012

Finance Track The SAP ERP Financials Solution Powered by the SAP HANA platform

© 2012 SAP AG. All rights reserved. 3

Agenda

WHY do we talk about SAP BW 7.30 on HANA in Financials?

Enterprise Data Warehouse in our Financials Solution

Corporate functions enabled and supported by in-memory technology

WHAT is new, when SAP BW runs on a HANA powered DB?

Main advantages and process improvements

HOW: Architectural changes to enable these improvements

WHO is benefitting from BW 7.30 on HANA?

Acceleration of central corporate business functions

Acceleration of Financials Reporting

Financials Solution powered by SAP HANA

WHY do we talk about BW 7.30 on HANA in Financials


Financials Solution Architecture:

Horizontal and Vertical Integration

Financial

Transfer

SAP LT

Business Reporting and Analytics

Central Financial Accounting

SAP ERP 6.0

Integrated Planning Consolidation

Enterprise

Performance

Management

MS Office On Demand

Services

Browsers Mobile

Information

Consumption

Reporting &

Analytics

Enterprise

Applications

Central Journal

= Real Time Replication with SAP Landscape Transformation Platform

Enterprise

Portals Embedded in

Enterprise Apps

(

…

)

Non-SAP

Any DB

In Memory

Enterprise

Data

Warehouse

Business Warehouse powered by

HANA

HANA Database


SAP BW, powered by SAP HANA - Overview Complete coverage of local, operational and corporate needs

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 Business Suite

SAP HANA SAP BW

Enterprise Data Warehouse

Design and Operations

Analytic Engine (Queries)

SAP BW Architected Data Marts

Data Acquisition (DataSources)

SAP HANA Models

SAP BusinessObjects BI

Replication Services SAP BusinessObjects

Data Services

SAP BW ETL Connectivity

extraction

logic

realtime

replication

data

harmonization

analytic

requirements

Corporate View on Information

Standardization

Operational View on Information

Agility

Feed SAP BW or SAP

HANA through Extractors

or SBO DataServices

Agile SAP HANA Modeling to

complement corporate SAP BW

Models for ad hoc business needs

SAP BW Analytic Engine to offer complete

set of OLAP functionality on top of SAP

HANA Calculation Engine

Integrate SAP BW ABAP lifecycle management

with SAP HANA Operations

Integrate

corporate

information into

data marts


(Corporate) Functions enabled and powered

by SAP HANA

Native (new)

functionality on SAP

HANA

SAP Consolidations

SAP Integrated

Planning

… more to come…

SAP Analytics

CO-PA Accelerator

HANA Accelerators

(side-by-side scenarios)

Finance & Controlling

Accelerators

Financials Accounting

Controlling

Material Ledger

Production Cost

Analysis

HANA Application

Accelerators

Project System

Reporting

Sales Order

Reporting

FI Open Item /

Aging Reporting

… Use your

imagination …

BW based functionality ERP based functionality ERP based functionality


WHAT is new in BW 7.30 on SAP HANA?


SAP NetWeaver BW7.3 Powered by SAP HANA How Does BW 7.3 Running on HANA Differ from BW Running on xDB ?

SAP NetWeaver BW 7.x on xDB

Standard DataStore Objects

Data Base server and SAP NetWeaver BWA

Standard InfoCubes

BW Integrated Planning

HANA Data Marts running side-by-side BW

SAP NetWeaver BW 7.3 on HANA

SAP HANA-optimized DataStore Objects

SAP HANA In-Memory platform

SAP HANA-optimized InfoCubes

In-Memory planning engine

Consumption of HANA artifacts created via HANA studio

BW staging from HANA

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.

Migration without reimplementation - no disruption of existing

scenarios


DataStore Objects in SAP NetWeaver BW 7.3 Overview and Challenge

DataStore Objects are fundamental building

blocks for a Data Warehouse architecture

They are used to create consistent delta

information from various sources

Reporting can be done on a detailed

level

In today's RDBMS-based

implementation, the activation and

querying operations are extremely

performance-critical

Active Data Table Change Log

Activation Queue

Query Delta upload

Parallel Upload

Activation


DataStore Objects in SAP NetWeaver BW 7.3 Creation of Consistent Delta Information

Traditional Architecture

Activation algorithm calculates the

changes of each record and creates heavy

load on the DBMS

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 Queue

Sorted Full Table Scan

Data

Packages

Lookup Calculate

Delta Update

Active Data Table Change Log


SAP HANA-optimized DataStore Objects Accelerated Data Loads

SAP 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 SAP HANA-optimized DSOs

No changes of Dataflow 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 HANA xDB



SAP NW BW


SAP HANA-optimized DataStore Objects Performance Figures

BW 7.30 - RDMBS based

Runtim

e in

seconds


SAP HANA-optimized DataStore Objects Performance Figures

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


New InfoCube Design

SAP HANA-optimized InfoCubes Faster Data Loads and More Efficient Data Model

MD

F

Facts

MD

MD MD

Traditional InfoCubes tailored to a relational DB consist

of 2 Fact Tables and the according Dimension tables

SAP HANA-optimized InfoCubes represent “flat” structures

without Dimension tables and E tables*:

Up to 5 times faster data loads (Lab Results)

Creation of DIM Ids no longer required

Simplified Data modeling

Faster remodeling of structural changes

After the upgrade to BW7.3, SP5 all InfoCubes remain

unchanged

Tool support for converting standard InfoCubes

Preliminary lab result: 250 Million records in 4 minutes

No changes of processes, MultiProvider, Queries required



*Tables for compressed data

Facts

D

D

MD MD

MD MD

F E

Conversion / New


Query performance Proven Query Performance as Known from BWA

Query acceleration on BW InfoCubes No replication – fast query access directly on

primary data persistence

Indexes on InfoCubes and InfoObjects no

longer required -> No Roll-ups, Change runs

In-memory Calculation Engine

– TopN, BottomN,

– Exception aggregation

– Currency conversion – . . .

Snapshot Indexes for Virtual- and QueryProvider

Query acceleration on BW DataStore

Objects (DSO)

Acceleration via In-Memory column storage

Additional acceleration via Analytic Views on top of DSO

No changes of processes, MultiProvider, Queries required

SAP NW BW

Query on InfoCube,

Masterdata AnalyticIndex,

CompositeProvider

Query on

DSO, BW InfoSet

SAP HANA

SQL Engine Calc Engine

Aggregation Engine on In-Memory data




Consumption of SAP HANA Data Models Tight Integration SAP HANA Data Mart Scenarios and SAP NetWeaver BW

CompositeProvider

InfoCube Transient

Provider

Query Query

BW schema HANA schemas

AnalyticView SAP

HANA

SAP NW

BW

HANA Data Marts and HANA In-Memory

platform for BW can run in one instance

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 InfoProvider dynamically generated on

top of Analytic Views during Query runtime

Query: e.g. Analysis, Xcelsius, Web Intelligence

(Web I)

Integration BW Analysis Authorization Concept




BW In-Memory Planning Accelerated Planning Functions

Traditional Planning runs planning

functions in the App. Server

In-memory Planning runs all planning

functions in the SAP HANA platform

Performance boost for planning

capabilities like:

Aggregation, Disaggregation

Conversions, Revaluation

Copy, Delete, Set value, Repost, FOX

Performance boost for plan/actual

analysis

No changes of planning models,

planning processes, MultiProvider,

Queries required



Database

Layer

Database

Layer

User interface

Layer User interface

Layer

Application

Layer Application

Layer

Presentation

Orchestration

Calculation

Data

Presentation

Orchestration

Calculation

Data

SAP NW BW

SAP NW BW

SAP NW BW

SAP

HANA xDB

SAP NW BW


Integrated Planning on In-Memory Database


In-Memory Database

S

AP

HA

NA

SA

P N

W B

W

S

AP

NW

BW

Classic Database

In-Memory Planning The Technological Change

xD

B

S

AP

NW

BW

SA

P N

W B

W

Presentation

Orchestration

Calculation

Data

Presentation

Orchestration

Calculation

Data

User

interface

Layer

Application

Layer

Database

Layer

Same user experience

• runtime UI

• modeling UI

Fully compatible

• Same models

• Authorization, locking

• List-based operations

… with faster

• Planning Functions

(Copy, distribute, Set value, …)

• Disaggregation in the query


Current Planning Architecture

Database

Layer

Application

Layer

User interface

Layer Presentation

BEx query

Plan Session

Data

Meta data

Delta buffer Local cache

Constraints

Logic

save

Aggregation

Engine

Small data flow

Large data flow


In-Memory Planning Architecture

Database

Layer

Application

Layer

User interface

Layer Presentation

BEx Query

Plan Session

Data index

Meta Data

Delta Buffer

Constraints

Logic

save

Meta Data Aggregation

Engine

Small data flow

Large data flow


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


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


In-Memory Planning Simple Disaggregation Example 2

Traditional Approach


2. Disaggregate (in application server)

per week (52)

per branch (500)

26000 combinations / values

3. Send 26000 values to DB to save

HANA-Based Approach


2. Send 1 value to HANA database

+ instruction to disaggregate and

how

3. Disaggregate (in HANA engine)

per week (52)

per branch (500)

create + save 26000 values

user changes

a plan value


Server-side value proposition

• existing feature set (full compatibility)

• integration of reporting and planning

(no redundancies)




(no redundancies)

• in-memory acceleration of reads




(no redundancies)

• in-memory acceleration of core functions

• further investments into performance

and functionality

Deployment Options for BW Planning 7.30

Classic DB

Database (SQL)

HANA

Database (SQL)

Calculation engine

Planning engine

HANA

Database (SQL)

Calculation engine

Planning engine

SAP BW

Integr. planning

Standard without in-memory Standard with in-memory Deep in-memory integration

SAP BW

Integr. planning

SAP BW

ABAP planning applications kit

User interface User interface User interface

BEX AAO BEX AAO BEX AAO

A B C





(no redundancies)




(no redundancies)





(no redundancies)



and functionality

Deployment Options for BW 7.30 Actions on Database Level

Classic DB

Database (SQL)

HANA

Database (SQL)

Calculation engine

Planning engine

HANA

Database (SQL)

Calculation engine

Planning engine

SAP BW

Integr. planning


SAP BW

Integr. planning

SAP BW




A B C

Database migration

Nothing to do





(no redundancies)




(no redundancies)





(no redundancies)



and functionality

Deployment Options for BW 7.30 Actions on Application Layer and Frontend Layer

Classic DB

Database (SQL)

HANA

Database (SQL)

Calculation engine

Planning engine

HANA

Database (SQL)

Calculation engine

Planning engine

SAP BW

Integr. planning


SAP BW

Integr. planning

SAP BW




A B C

Toggle switch, see

SAP Note 1637199

Nothing to do


WHO is benefitting from BW 7.30 on HANA?


Major Benefits for YOU

No BWA needed

Data do not need to be indexed anymore, no

BW-Accelerator maintenance needed

Replacing the traditional database below BW

Reduction of Data redundancy

Storage Space is reduced dramatically

No new Cubes needed

Direct consumption from DSO’s and HANA views

Keep your traditional InfoCubes

Faster structural changes – agility for business users

Efficient loading process

Data is available for reporting faster, no need for “overnight extractions”

anymore

Faster integrated planning

Spend more time on simulations and analysis

All existing

content and

other

investments

is fully

compatible

and secured

Thank You!

SAP Americas Inc.

Lars Buescher

SAP Active Global Support

Solution Architect

T: +1 484-634-6531

E-Mail: [email protected]

SAP Americas Inc.

Gilberto Henn

SAP Active Global Support

Director – CoE Financials

T: +1 610-618-0838

E-Mail: [email protected]

Documents

SAP Innovation Summer Camp Come with a Curiosity – …fm.sap.com/data/UPLOAD/files/08 SAP_Innovation... · SAP Innovation Summer Camp Come with a ... DataStore Objects in SAP NetWeaver