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EDW Positioning Based on the SAP Real-Time Data Platform July, 2013

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EDW Positioning Based on the SAP Real-Time Data Platform

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Page 1: Enterprise Data Warehousing Positioning

EDW Positioning Based on the SAP Real-Time Data Platform

July, 2013

Page 2: Enterprise Data Warehousing Positioning

Typical needs in a Enterprise world

Introduction

Page 3: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 3

Types Analytics & Data Marts

Different Analytical needs and the consequences in IT architectures

Complex architecture landscapes including different kind

of Data Marts, Data Warehouses and EDWs

Operational

Data Mart

Visibility

Direct / Database

Replication

Near-line

Data Marts

(Audit / SOX)

Federated

Queries / ETL

Agile

Data Marts

Ad-Hoc | Self-

Service BI

ETL / ELT/

Replication

Data

Warehouse/

EDW

Consolidated

view

ETL / ELT /

Replication

Transactional |

System of Record

Transactional | Analytical |

Systems of Record & Engagement

Real Time

Data Mart

Real Time

Streams / Feeds

Event | Machine

ETL / ELT/ Replication

Federation of Data & Query

All sources

Predictive

Data Marts

Unstructured

Data Marts

Sentiment

Analysis

Archived

Information Social

Architecture

Information needs

Source of

information

Access method to

source data

Predictive

Analysis

Archive

Analysis

High variety of information sources

Extensive information needs

Page 4: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 4

Different Needs … Different Types of Data Marts for Reporting

Characteristics of Data Marts:

• Maximum flexibility for LOBs in data modeling

and integration of LOB specific data (Agile)

• Supporting local business execution

• Support strategic decision making in LOBs

• Independently of, or sourced from the centralized corporate

EDW layers

• Reporting on large volumes of granular, transactional data

• Data latency determined by business requirements – batch

to real time

• High Volume Unevenly structured data (Big Data)

• Predictive Analysis

• Event and Streaming sources

A data mart is a collection of data coming from subject

areas organized for decision support based on the needs

of a given domain / group of users.

Page 5: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 5

Examples of Different Types of Data Marts

Business Intelligence Tools

Supplier Data

Operational

Real-time

replication and

ETL

ETL

ERP Data Other EDW

Agile DataMart Operational DataMart

Operational Data Marts:

• Real Time Data

• Reporting on large volumes of granular,

transactional data

• Supporting local business execution

Agile Data Marts:

• Independently of the highly governed

centralized corporate EDW layers

• Maximum flexibility for LOBS in data

modeling and integration of LOB

specific data

• Support strategic decision making in

LOBs

Page 6: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 6

Unlock The Power of Your Data Across The Enterprise Enterprise Data Warehousing – the single point of truth

EDW * – characteristics:

• Consolidate the data across the enterprise to get a

consistent and agreed view on your data

• Combine SAP and other sources together

• Standardized data model on corporate information

• Supporting decision making on all organizational levels

EDWs require a database plus EDW orchestration

tools:

• EDW orchestration tools are used to manage, model and

run the Enterprise Data Warehouse

• EDW orchestration tools can be shipped as an integrated

EDW application

• Custom built EDWs are based on loosely coupled

orchestration tools e.g. ETL tools, Data Modeling tools,

Monitoring tools etc.

* EDW and DW are often used interchangebly. For the

remainder of this presentation only the term EDW will be used.

Page 7: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 7

global

local

regional

Master Data

Management

Business Intelligence Tools

Planning &

Forecast

feeding

external

systems

Example of a Customer Enterprise Data Warehousing landscape

DataMart

DataMart

Master Data

Transactional Data

Example for global EDW

implementation: Architecture to provide the base for

entire commercial business intelligence

solutions:

• Local, regional, global Data Marts

in addition to global Data Warehouse

necessary to meet business users

need

• Data Warehouse as single point of

truth with harmonized master data

to feed departmental Data Marts

and external systems with cleansed

data

• Depending on users requirements

reporting on departmental Data Mart

or Data Warehouse is possible

Data Warehouse „Single Point of Truth“

ERP Data Supplier Data Other Customer Data

DataMart

Page 8: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 8

Different Approaches What approach is right for you?

Different approaches to implement an analytical data foundation

• Using an Integrated EDW application

• Model-driven pre-packaged Data Warehouse application as a

central component

• Prebuilt Information – and process content

• Out of the box tool set for modeling, managing, operating, and

governing an EDW and its data marts

Custom Built Enterprise Data Warehouse

• Loosely coupled orchestration tools

• Higher efforts for development and maintenance

• High flexibility to build custom data models and processes with

little enforced governance

• Open environment to easily import industry models

Page 9: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 9

Different Approaches What approach is right for you?

• Custom built data marts without EDW integration layer

• Maximum flexibility for custom data marts specifically built for

isolated use cases

• Easy to build and fast realization time

• Low level of integration among different data marts

• Grow from custom built DMs to an EDW

• Combine custom built DMs or EDW with integrated EDW application

Combination of approaches

Page 10: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 10

Very large Data Marts

Data Volume and Complexity considerations Enterprise Data Warehouse, Data Marts

data

volu

me

huge

moderate Complexity: number of scenarios, data models, sources, … huge

• Mix of scenarios with small and

large amounts of data

• Many (100s to 10000s) of data

models

• Many (100-1000s) different data

sources

• Extremely high number of

scenarios and combinations of

scenarios

• Extremely high data volumes

• Internet scale business process

(e.g. Ebay, Amazon, …) generating

huge amounts of (sensor) data

• Fairly modest challenges regarding

semantics, consolidation, harmoni-

zation, integration with other data

Data Mart EDW

Extreme large EDW

• Few data sources

• Data mart type of setup or operational

(OLTP) analytics

• Moderate number of tables

• Moderate (need for) integrations between

data models

Page 11: Enterprise Data Warehousing Positioning

How does SAP address the needs?

Page 12: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 12

SAP Real-Time Data Platform Addressing the architectural needs

Custom

Apps

Mobile &

Embedded

Business

Intelligence

Business

Suite

Business

Warehouse

SAP RTDP addresses the needs for a packaged

EDW framework with SAP BW, as well as custom

built EDW and Data Marts

• SAP NetWeaver BW as strategic offering for packaged

EDW frameworks

• SAP HANA as an in-memory foundation for custom built

solutions and in-memory database platform for BW

• SAP Sybase IQ as disk-based database for custom built

petabyte scale EDW and cost effective near-line storage

solution (NLS) for BW on HANA

Page 13: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 13

SAP HANA, BW on HANA, Sybase IQ Strong individual solutions

Sybase IQ

BW on HANA

SAP HANA

SAP HANA

• Data Platform for business- or mission-critical enterprise applications

• In-Memory database for transactional and analytic workloads

• More than 1000 customers with standalone, BW or Business Suite scenarios

SAP NetWeaver BW

• More than 14000 customers referring to > 17000 productive systems

• Vast majority: Central EDW, harmonizing many source systems

• Embedded into mission critical business processes

Sybase IQ

• Recognized leader in EDW market by Gartner and Forrester Group

• Industry leading performance and scale benchmarks with 4500+ installations in 2150 accounts

• Greater than 96% customer satisfaction rates - consistently

Page 14: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 14

Data Modeling • Layered scalable EDW model

• Tight integration with native HANA Data Marts

• In-Memory based OLAP features and

Planning engine

• Agile Data Marts with BW Workspaces

Scheduling & Monitoring • Process Chains enable workload management for

data load processes across systems

• Rich monitoring capabilities via Admin Cockpit

• Support of 3rd party tools such as UC4, Tivoli

Supportability, Traceability • Easy extensible BADI framework for data staging,

reporting and authorization

• Access statistics including Identity handling

• Possibility to trace on application logic, database

access and effect of authorizations

Reliable Data Acquisition • Open for SAP – and non SAP systems

• Embedded ETL and Streamlined In-Memory Data

Staging processes

• Sophisticated Error Handling

• Incremental Data Provisioning capabilities

Business Content • Enables fast implementation

• Leverages SAP’s proven knowledge in

business applications and industries

• Model-driven approach

EDW with SAP BW on HANA

Lifecycle Management & Security • Hot- / Cold data handling for optimized resource

usage

• Compliant NLS and Archiving

• Fine-grained security model with mass handling

capabilities

Page 15: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 15

Data Modeling • Entity-Relationship models

• Procedural extensions for complex views

• Support for planning operations on views

• Real-time transformation 3NF to analytic view

Scheduling & Monitoring • DB level monitoring in SAP HANA Studio

• Explain plans for SQL statements

• No scheduling capabilities, requires additional tools

such as SAP Business Objects Data Services

Supportability, Traceability • Technical tracing of database kernel operations

• Alerting for vital database parameters

• Integration with SAP Solution Manager

Reliable Data Acquisition • Support for virtually any kind of data

• Support for SAP or 3rd party ETL tools

• Real time replication and event stream connection

Business Content • Business Suite data models available as

content packages (SAP HANA Live, RDS)

• Application content available as delivery units

Lifecycle Management & Security • Repository for design time artifacts

• Database security with user and role concept

• Privilege concept for design time and runtime

• Content lifecycle management

EDW with SAP HANA

Page 16: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 16

Data Modeling • Leading open modeling tool: PowerDesigner

• High flexibility in schema definition

• Ad-Hoc SQL, structured and unstructured data

support, and integrated full text search

• In-database analytics, MapReduce API, Hadoop

and R-Integration

Scheduling & Monitoring • Web-based SAP Sybase Control Center as DB

administration & monitoring tool

• ETL scheduling with DataServices

• Open and certified support for 3rd party monitoring

solutions

Supportability, Traceability • Database auditing: logins, data update

timestamps, and administrative actions

• Support of multiple trace and debug levels

• Extensive set of system stored procedures for

diagnostics

Reliable Data Acquisition • Open for SAP – and non SAP systems

• Data Services as powerful, approved ETL tool

• Open and certified support for 3rd party ETL solutions

• Real Time Data Loading via ESP and SRS

Business Content • Prebuilt vertical Industry Warehouse Solutions

• Open and certified support for many 3rd party

applications

EDW with SAP Sybase IQ

Lifecycle Management & Security • Built-in disk–based Information Lifecycle Management

• Role-based access control, LDAP authentication, strong

database, column, and transport layer encryption (FIPS

certified)

• New generation petabyte-scale store

• Resilient Multiplex grid architecture

Page 17: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 17

SAP HANA, BW on HANA, Sybase IQ - Integration scenarios

Integration scenarios:

• Tight integration between BW on HANA and native

HANA scenarios combines the strengths of both worlds

• Consumption of BW data models in HANA and

consumption of HANA models in BW

• Data staging from HANA to BW and from BW to HANA

• IQ supports NLS (multi temperature Data) for BW on

HANA and remote data access from HANA for smart

query processing

• Lower TCO by reduced data volumes in HANA and

shortened backup time frames

• Cost – and Performance optimized EDW

• Mature EDWs will leverage the capabilities of all three

components where appropriate

BW on HANA Sybase IQ

Packaged

EDW

framework

Custom built EDW

Tight

Integration

between BW on

HANA and SAP

HANA

SAP HANA

Smart Data

Access

Sybase IQ

as NLS for

BW

Custom built

EDW

Petabyte

scenarios Sweet Spot

for Mature

EDW

Page 18: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 18

Customer requires standalone Data Marts

• HANA provides maximum agility for custom built data marts

• With increasing BI maturity* customers are able to grow from HANA based Data Marts to an EDW

on HANA

Customer requires operational analytics directly on the original transactional data

• SAP HANA Live for SAP Business Suite (see appendix for more details)

Customer requires an EDW

• BW on HANA is SAP‘s strategic EDW solution and should be considered as a cornerstone for

customers‘ EDW strategy

Customer requires an EDW for very large data volumes

• Sybase IQ recommended for Petabyte scale

Customer requires an NLS solution

• Sybase IQ provides very low TCO for advanced data lifecycle management capabilities, e.g. NLS

Conclusions – Isolated view

SAP HANA

* TDWI BI Maturity Model

BW on HANA

Sybase IQ

Page 19: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 19

• BW on HANA and HANA Agile Data

Mart scenarios perfectly address the

needs of Central EDWs and Data Marts

• IQ is SAP‘s native NLS for BW on

HANA and is capable of storing

terabytes to petabytes of structured &

unstructured data

• Mature customer architectures will

leverage the capabilities of all three

components where appropriate

The HANA EDW

SAP HANA, BW on

HANA + IQ NLS

Conclusion – The HANA EDW Combining the strengths of the different worlds

Sybase IQ

BW on HANA

SAP HANA

Page 20: Enterprise Data Warehousing Positioning

Thank you Contact information:

Product Management: Lothar Henkes, Courtney Claussen, Brian Wood

Solution Management: Daniel Rutschmann

Consulting: Andreas Scholl

COE: Lars Jakob

CSA: Rudolf Hennecke

Page 21: Enterprise Data Warehousing Positioning

© 2013 SAP AG. All rights reserved. 21

Further Information

Find additional information

• www.SAPHANA.com

• Real-Time Data Platform landing page: www.sap.com/RTDP

• SAP NetWeaver Business Warehouse 7.4:

http://scn.sap.com/docs/DOC-35096

• BW on HANA FAQ: http://spr.ly/bwonhanafaq

• Roadmaps: http://service.sap.com/roadmap

Link to EDW Positioning presentation:

• http://www.saphana.com/docs/DOC-2164

• https://scn.sap.com/docs/DOC-41326

Page 22: Enterprise Data Warehousing Positioning

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