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DMM104 – SAP HANA Data Warehousing:

Overview, Components, and Future Strategy

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 2 Public

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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

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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,

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Speakers

Bangalore, October 5 - 7

Shanmugam, Velliangiri

Las Vegas, Sept 19 - 23

Marc Hartz

Barcelona, Nov 8 - 10

Marc Hartz


Why is Data Warehousing still necessary?

Characteristics

Consolidates data across the

enterprise

Standardized data model

Supports decision making

Main Tasks

Define common semantics

Harmonize data values

Establish a ‘single version of truth’

Provide a single, comprehensive

source of current and historical

information

SAP S/4HANA SAP ERP Non-SAP

Analytics (BI, Predictive, Planning)

Data Warehouse “Single Point of Truth”

C4C

Emb. Analytics

SFSF

feeding

external

systems

Planning

&

Forecast

virtual access ETL

Hadoop

„data

lake“

SAP HANA Vora

Cloud for

Analytics


Market Expectations

Gartner 1 “Emerging data sources, trends and technologies challenge the

effectiveness of data warehouses in supporting analysis and decision making.”

IDC 2: “ The data warehousing market based on relational databases will

continue to be disrupted by several nonrelational and/or nonschematic

information management software categories. Data warehouses will not

disappear as they have a key place in an organization's data architecture.”

*1 ”2016 Strategic Roadmap for Modernizing Your Data Warehouse Initiatives” Mark Beyer and Lakshmi Randall, Gartner, October 2016

*2 Worldwide Business Analytics Software Forecast, 2016–2019 by Dan Vesset et al, IDC, July 2016. Doc # 257402


Current Portfolio – Tool Use Cases

SAP Agile Data

Preparation /

Information Steward

SAP Power

Designer

SAP HANA

Modeler /

SAP Web IDE

• Data Lineage: monitor flow of data across

models (until avail. via Information Steward)

• Empowers to discover, assess, define,

monitor and improve data quality

SAP BW

• Conceptual & Logical Modeling,

Reverse-Engineering, Impact Analysis,

Model Comparison, New Model

Versioning

• Technically implement and model

SAP HANA Artifacts

• Administrate SAP HANA models

• Full embed a featured application server,

web server, and development

environment

• Provides ETL services & data

replication, advanced data transformation

and data quality functionality

• Design & Implement Data Flows

• Data Distribution Optimizer for efficient

administration of large environments

• Data Life Cycle Manager for data

temperature /aging management

SAP HANA

extended appli-

cation Server

SAP HANA

EIM Service

SAP Application

Lifecycle

Management

SAP HANA Data

Warehousing

Foundation

• Data Warehouse application with

complete stack for modelling, ETL and

life cycling

• Organize SAP HANA Artifacts to Delivery

Units for transports across systems

(Versioning, Revisions)


SAP HANA DW – Strategy

Execution and delivery

2016 - 2018 Vision

Planning and definition

2015

Analytics

(SAP BI Suite, Predictive, Planning)

SAP HANA DW

SAP HANA DW

SAP HANA DW

SAP DW

Foundation

SAP Power

Designer

SAP HANA

EIM

SAP BW

SAP HANA Plattform

Market presence in data warehousing

with a clear roadmap

Strong and simplified

offering with tight integration

Convergence into one technology stack

addressing BW and SQL-based DW needs

SAP DW

Foundation

SAP Power

Designer

SAP HANA

EIM

SAP BW

DW Modeling DW ETL & DM

SAP HANA Plattform

Analytics


Analytics


SAP HANA Vora

SAP HANA

Vora SAP HANA Plattform SAP HANA

Vora


SAP HANA DW – Future-Proof Data Management Platform for

Analytics

Serve standard SQL-based and BW-style data

warehousing in order to …

meet future demands

• LDW for dynamically changing system landscapes

• Cloud and hybrid deployment

• Integration of any data types and Big Data

technologies

• Scale out to high volumes and data lakes

go beyond other DW offerings

• Top out-of-the-box integration to SAP solutions –

on-premise and in cloud environments

• Real-time processing power of SAP HANA

• Hadoop integration with SAP HANA Vora

• HANA-based analytic business services

• HANA-optimized re-usable business content


Planned Delivery Focus

Data Management &

Processing

Data Access Services End-to-End Operations

Modeling & Metadata

SAP HANA EIM becomes the central data

integration component of the SAP HANA DW

Flexible adapters for logical data warehousing

covering SAP, third party and Big Data

sources

Unified data processing across databases and

data lakes

High performance business services, e.g. for

inventory handling, planning and resource

allocation

Uniform scheduling and monitoring services

for SAP HANA DW data flows

Advanced data distribution services for scale

out and dynamic tiering

Comprehensive life cycle services for SAP

HANA DW components

Integrated top-down modeling of DW artefacts

with SAP Power Designer

Consistent release management and impact

analysis across DW models

Consolidation of BW modeling objects

optimized for SAP HANA


SAP HANA DW – Component View

Web Modeling

SAP HANA

XS advanced SAP HANA EIM Service

SAP HANA Application

Lifecycle Management

SAP HANA Data

Warehousing Foundation

SAP Agile Data

Preperation

SAP Power Designer

SAP Web IDE SAP BW

SAP HANA data warehouse

Plug-In Editors

Git Hub

HDI*

* SAP HANA Deployment Infrastructure

SAP HANA SQL DWH

(aka native)


SAP HANA XS Advanced development platform

Run and scale applications on premise and in the cloud

• Polyglot runtime containers for SAP HANA application

development like Node.js or Java

• New SAP HANA Deployment Infrastructure (HDI)

• Multiple times deployment of isolated

native SAP HANA content

• Established standard development tools and processes

• E.g. Git for version control

• Run SAP XS classic applications (XSJS) as first class citizen of

SAP XS Advanced

• Unified web based development environment for end-to-end

native SAP HANA applications

SAP Web IDE for SAP HANA External

development tools

XS Advanced runtime

External Repository

(GIT)

SAPUI5 & Node.js Dev Tools

SAP HANA Dev & Modelling Tools

XSJS

SAP HANA Database

Database

SAP HANA Deployment Infrastructure

Node.js Java …


SAP HANA EIM: Next generation data integration and data quality Design once, execute anywhere

IT User

SAP HANA

Web IDE

Business User UI

SAP Agile Data

Preparation

Flowgraph: DI & DQ

XML

De

sig

n

Ex

ec

ute

SAP HANA

Integration & Quality Services

Secure, Performance, Monitor, Manage, Accessible

Smart Data Integration Smart Data Quality Smart Data Streaming

ON-PREMISE | CLOUD | HYBRID Distributed Data

Processing

Native Data

Processing

Any DB

Google

Hadoop

Amazon

Microsoft

Azure

Prebuilt: Common Data Model

S4H, Ariba, SFSF, Concur,

Hybris, Fieldglass, C4C

SAP HANA Platform

Data

Lin

eag

e / Im

pac

t A

naly

sis

HANA

Repository


SAP Agile Data Preparation – Key Capabilities Discover, Profile, Combine, and Share Data Sets

SAP Business

Suite

SAP BW

Databases

Delimited

Files

SAP HANA

Sources Prepare Targets

distribute

Explore

Shape

Combine

Clean

SAP Agile Data

Preparation

Govern

Analyst, Scientist,

Steward

IT

Delimited Files

SAP HANA

Enrich

ingest

1 Ingest data from

variety of sources

2 Profile data

3 Combine, shape,

enrich, or cleanse data

4 Output data for

downstream uses

5 IT Governance team -

analyze and optimize

user processes

De-dup.


SAP HANA DW – SQL based DWH (Example)

on premise

UI5 / SAP BI

SAP HANA DW

IBM DB2 ORACLE

XS Advanced

MSSQL

Cloud / OnPremise

Power

Designer

Web IDE

(DevX)

Calculation Views (.hdbcalcview)

persisted Table (.hdbcds) virtual Tables

(.hdbvirtualtable) Raw

Data

Layer

DWH

Layer

Data Mart

Layer

FlowGraph (.hdbflowgraph)

HALM*

EIM Services

SDI SDQ ESS

HDI

DWF

DESIGN TIME RUNTIME

(.hdbflowgraph)

(.hdbcds / .hdbcalculationview / .hdbvirtualtable …)

MTA

DDO DLM

Consume / Access

DB Cockpit

Streaming ETL ELT ETL / SOA Load Replicate ETL Virtual Access

Agile Data

Preparation

(ADP)

HANA EIM (SDI, SDQ, ESS)

*planned


SAP HANA DW – Customer Example

Overview

SAP HANA, SAP PowerDesigner, and a number of other SAP

as well as non-SAP tools that are integrated in the solution.

“Data vault” modeling as best fit, as it provides the full data

traceability and historization a financial institution needs

Benefits & Goals:

Consolidation of several data warehouses into one

Reduction of interfaces a single source of truth

Traceability of all data and processing

High performance reports and simplified slicing and dicing

Free choice of tools and data schema provided optimal fit for

the customer’s scenario

SAP BW powered by

SAP HANA


SAP BW – Customer Example

Business Value

Consolidated, performant analytics out of one hand has

greatly improved the user experience.

Financial management is tighter and timelier with access

to key reports in seconds.

Landscape consolidation has greatly simplified the IT

landscape and improves IT’s support for the business.

Technical Impact

Highly simplified data model

Reports over a billion rows available in seconds rather

than minutes

Data load times reduced by over 60%

Largely avoided building of InfoCubes by using data

store object (DSO) layers and Composite Providers


• Semantic Groups for Advanced DataStore Objects

• Enhancements for CompositeProvider

• Additional Object Editors in Eclipse

• InfoObjects Supporting Transitive Attributes

• Streaming Process Chains

• New BW Workspaces Scenarios

Highlights of SAP BW 7.5 SP4

19 Public © 2016 SAP SE or an SAP affiliate company. All rights reserved.

Simplification Big Data

• Extension Nodes as New “Warm” Data Concept

• Extended Near-line Storage Capabilities

• HADOOP as Near-line Storage

• SAP HANA Smart Data Integration

via New HANA Source System in BW

• SAP HANA Optimized Transformations

Platform Integration

• Reduced Governance and Faster Time to Market

• Run SAP BW in Simplified Mode

• General Availability Planned for September 2016

SAP BW 7.5, edition for SAP HANA

Mixed Scenarios


Mixed Scenarios Motivation

Create Synergies between SAP BW and SQL based DWs

Combine both approaches on one platform

Simplification – reduce data movement

Consistency – minimize data redundancy

Modern, flexible Data Warehousing

Strong virtualization capabilities of SAP HANA

Mature data management capabilities of SAP BW on HANA

Create flexible data models that can adapt to changing requirements

Openness

Connect any frontend solution to SAP BW

Directly connect SQL Tools to SAP BW

Simplify consumption of external data in SAP BW

SAP HANA

SAP BW

Sources

BI Clients

HANA Modeling BW Modeling

<

Mixed Architecture


SAP HANA DW – Mixed Scenario Customer Example

The benefits of the mixed approach are in this case:

• Efficient handling of data volumes for cost optimization

• Driving virtualization without sacrificing performance

• Advanced DataStore Objects and Open ODS Views relieve

need to create InfoObjects and speed up any prototyping

activity

• Frequently used data sets can be persisted for performance

and reusability purposes

• Excellent query and data load performance

• SAP HANA automatic view generation reduces time to develop

with gain of flexibility

• Benefits from SAP BW’s excellent delta handling for data loads

Summary


SAP HANA DW – Summary

SAP HANA DW – a flexible and modern

data warehouse framework • SQL centric approach as a scenario for data

warehousing with tooling for implementation

• SAP BW as the guided and integrated data

warehouse

• Mixed Scenarios as best reference

• Independent from starting point HANA DW

components can be added and used in a mixed

architecture at a later point in time


Further information

Related SAP TechEd sessions:

DMM265 – SAP HANA Data Warehousing: Introduction to Data Modeling in SAP HANA

DMM213 – SAP HANA Data Warehousing: Data Lifecycle Management and Data Aging

DMM270 – SAP HANA Data Warehousing: Simplified Modeling with SAP BW 7.5 SP4

DMM272 – SAP HANA Data Warehousing: Mixed Scenario for SAP BW and SQL DW on SAP HANA

DMM300 – Mixed Scenarios for SAP HANA Data Warehousing: Overview and Experiences

Hands-On Workshop

Lecture

Hands-On Workshop

Hands-On Workshop

Lecture

SAP Public Web

https://scn.sap.com/docs/DOC-66016 – Best practice paper on SAP HANA dynamic tiering

http://scn.sap.com/community/information-lifecycle-management – SCN space for ILM

SAP Education and Certification Opportunities

www.sap.com/education:

SAP training curricula: HANA – S/4HANA – BW

Watch SAP TechEd Online

www.sapteched.com/online

https://scn.sap.com/docs/DOC-66016



http://scn.sap.com/community/information-lifecycle-management





http://www.sap.com/education

https://training.sap.com/de/en/courses-and-curricula/hana

https://training.sap.com/de/en/courses-and-curricula/s4hana

https://training.sap.com/de/en/courses-and-curricula/netweaver

http://www.sapteched.com/online


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