Internal

SAP Modern Data Platform Cubis - Enterprise Information Management

Henrik Kummel, SAP Database & Data-management GTMSAP EMEAMay, 2017

Data Sources, Access Methods, and Information Need

Information need Real time VisibilityConsoli-

datedAd hoc

Archive and

analysisPredictive Sentiment

Source

Architecture

Access method

Real-time

data mart

Operational

data mart

Data

warehouse

Agile data

mart

Near-line

data mart

Predictive

data mart

Unstruc-

tured data

mart

Streams

Direct,

database

replication

ETL and ELT

replication

Federation

and ETL

ELT, ETL and federation of

data and queries

Sensors,

IoT and

events

Trans-

actional

systems

Transactional and

analytical systemsSource Any Social

Hot Data Warm Data Cold or Frozen Data Candidate Data

Data Value

Complexity (number of data models, sources, …)

Data

Volume

Huge

Modest

Modest Huge

Data Warehouses

▪ mix of scenarios with small and

large amounts of data

▪ many (1,000s to 10,000s) of data models

▪ many (100s to 1,000s) different data

sources

Extremely Large Data Warehouses

▪ internet scale business processes

▪ many data models

▪ many different data sources

▪ Data Lakes

Data Marts

▪ data mart or operational

analytics

▪ modest number of tables

▪ modest need for integration

between data models

Very Large Data Marts

▪ internet scale business

processes

▪ modest challenges regarding

semantics, consolidation,

harmonization, etc.

▪ few data sources

Data Warehouse Scenarios

Redefining Systems of DifferentiationIncreased Visibility & Better Processes

Accelerate

departmental insight

with Agile Data Marts

Simplify cross

enterprise

transparency

with Modern Data

Warehousing

Extend analytical

context

with Data Lakes

Ensure confident

decision making

with Trusted Analytics

How to run

your company?

How to engage?

How to sense?

How to decide?

How to steer

your company?

Setting the foundation

Bi-Modal IT

Mode 1 Mode 2

Core Edge

Modernize your core and innovate at the edge

SAP

S/4HANA

How to engage?

How to sense?

How to decide?

SAP

Analytics

SAP HANA Platform

Bi-Modal IT

Mode 1 Mode 2

Core Edge

Modernize your core and innovate at the edge

The Core : SAP S/4HANA 1610

SAP S/4HANA

MARKETING AND

COMMERCE

SAP S/4HANA

HUMAN

RESOURCES

SAP S/4HANA

MANUFACTURING

SAP S/4HANA

SUPPLY CHAIN

SAP S/4HANA

SALES

SAP S/4HANA

FINANCE

SAP S/4HANA

SERVICE

SAP S/4HANA

SOURCING &

PROCUREMENT

SAP S/4HANA

ASSET

MANAGEMENT

Digital

CoreEnterprise Management

SAP S/4HANA

RESEARCH &

DEVELOPMENT

Options

Options

INDUSTRIES

The Core : SAP HANA Platform

Data Warehouse “Single Point of Truth”

Virtual Access ETL ELTStreaming

Data Sources

SAP, non-SAP, On premise, Cloud

AnalyticsBusiness Intelligence, Predictive, Planning

Data Lake

S/4HANA

IBM DB2, Netezza, Oracle, MS SQL

Server, Teradata, SAP HANA, SAP

ASE, SAP IQ

SAP HANA Digital HubProviding a Business Data Model with Provisioning Services

DATABASE SERVICES

Web Server JavaScript

Graphic Modeler

Data Virtualization ELT & Replication

Columnar OLTP+OLAP

Multi-Core & Parallelization

Advanced Compression

Multi-tenancy Multi-Tier Storage

Graph* Predictive Search

DataQuality

SeriesData

Business Functions

Hadoop & Spark Integration

Streaming Analytics

Application Lifecycle Management

High Availability &Disaster Recovery

OpennessDataModeling

Admin &Security

Remote Data Sync

Spatial

Text Analytics

Fiori UX

ALM

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APPLICATION SERVICES INTEGRATION & QUALITY SERVICESPROCESSING SERVICES

S A P H A N A D i g i t a l H u b

Truck

01001000

Click StreamMachine

BuildingMeterSmart Data Streaming

Remote Data Sync

Built-In Adapters Custom Adapters

ODataDB2, Oracle, MS SQL Server,

Teradata, SAP HANA, SAP ASE

Dynamic Tiering

Hot Warm

SAP IQ

Smart Data Access

Virtual Tables

Smart Data Integration

Adapter Framework

Metadata

R Integration

Spatial FunctionsSpatial Data Types

Maps & POIGeocoding

Spatial Engine

IOT

In-Memory

Digital Boardroom

Consumer

Grade

Applications

Audit proof “Sealed, Signed and

Delivered” data persistence Consistent Real-

Time data

Integrated operations, modeling

& development

Lightning fast,

in-memory

Access data in-place Enterprise-wide master data like

hierarchies, time-dependent data etc. Calculated and

restricted KPI’s

Provisioning

Agile/Virtual Data Model

Vora

Spark

Vora

Spark

Vora

Spark

Compiled

Queries

Spark

Controller

Drill Downs

Enterprise Data Warehouse“Single Point of Truth”

SAP HANA

Studio/Web IDE

Application Function

Modeler (AFM)

Tools &Applications

SAP

Predictive

Analytics

An

tivir

us

syslo

g

Sin

gle

Sig

n-O

n

Ke

rbe

ros

SA

ML

Ide

ntity

Mg

mt

SQ

L

Co

mp

lia

nce

SQ

L

Lo

gg

ing

NW

-VS

I co

mp

atib

le

Mobile

Open Analytics

SAP Business

Data

Data harmonization

Governance

Modern in-memory platform

Real-time transaction and analysis

Native predictive, text, and spatial

algorithms

Hot Data

SAP HANA

New: in-memory dynamic tiering

option

Disk-based, dynamic tiering option

using smart columns

High performance and efficient

compression

Excels at queries on structured data,

from terabyte to petabyte scale

No data duplication

Warm Data

SAP HANA

Dynamic Tiering

Data persistence optimized in system

landscape through relocation of

infrequently accessed data to SAP IQ

Less frequently accessed data

archived in time partitions

NLS data resides in a highly

compressed state in cost-efficient

storage with fewer backups to reduce

operational costs

Cold or Frozen Data

SAP IQ

Acceleration with SAP HANA Vora

SAP HANA smart data access

capability for Hive- and Spark-type

scenarios

Candidate Data

SAP HANA Vora

HADOOP

Data Lifecycle Management (temperatured data)

Modern in-memory platform

Real-time transaction and analysis

Native predictive, text, and spatial

algorithms

Hot Data

SAP HANA

New: in-memory dynamic tiering

option

Disk-based, dynamic tiering option

using smart columns

High performance and efficient

compression

Excels at queries on structured data,

from terabyte to petabyte scale

No data duplication

Warm Data

SAP HANA

Dynamic Tiering

Data persistence optimized in system

landscape through relocation of

infrequently accessed data to SAP IQ

Less frequently accessed data

archived in time partitions

NLS data resides in a highly

compressed state in cost-efficient

storage with fewer backups to reduce

operational costs

ILM will process the lifecycle postprod

Cold or Frozen Data

SAP IQ NLS

and ILM

Acceleration with SAP HANA Vora

SAP HANA smart data access

capability for Hive- and Spark-type

scenarios

Candidate Data

SAP HANA Vora

Spark & Hadoop

Data Lifecycle Management (temperatured data)

SAP Solutions

Marketplace

Ecosystem

APIBusiness Hub

Big Data

AltiscaleHadoop

SAP HANA Vora

SAP HANA Any DB

Security Integration AnalyticsMobile IoT ServicesUX & Collaboration

Machine LearningBusiness Services

SAP Cloud Platform

SAP Cloud Platform 2017 Spring Update

Development Services

• CloudFoundry

• Unified Web IDE for SAP HANA and Ux

Operations

• Data Center Expansion

• Virtual Machine Shared Storage

• HA & DR capabilities for improved SLA

• Leverage 3rd Party IaaS for SAP Public Cloud (AWS, Azure, GCP)

Data-Lake Capabilities

• HANA Data Tiering

• Big Data Support (Altiscale & Vora)

New Capabilities

• Machine Learning Services

• Video/Voice/Media Processing Services

• CoPilot – Digital Assistant

• Blockchain

Marketplace & Billing

• Transact 3rd party apps

• Utility Billing for some SAP Cloud Platform Services (V1)

IoT Capabilities

• High Volume Ingest (MQTT Ingest)

• Plat.One support for IoT Workflows & IoT Modeling

SAP Cloud Platform Big Data Servicesformerly known as Altiscale

Fully managed Big Data in the cloud

Hadoop- and Spark-based platform

Acquired by SAP, September 2016

Data-Lakes in the Cloud

Big Data is complexIt gets more complicated as you scale

Big Data as a Service

Sample Customers

Leading financial services

company

(confidential)

MarketShare Case Study

MarketShare DecisionCloud

Attribution analysis: measure, predict, and improve the impact of marketing on revenue

Customers: brand-name retail, finance, hospitality, pharma, auto, and tech companies

Massive, rapidly changing advertising data stored and analyzed in Hadoop

~2.5 PB of HDFS provisioned on Altiscale

MarketShare Case Study

Challenges due to inefficiencies of previous platform

Job processing taking too long

Poor service reliability

Product development hampered

Service costs driven up

Number of customers limited

Altiscale Benefits

• Greater client satisfaction due to higher

performance and reliability

• More time to focus on analytics instead of

Hadoop operations

• More effective resource allocation and

cost management

• Increased solution competitiveness

50% Lower Cost

10xPerformance

10x faster job completion

Expand perspective

City of Buenos Aires

Uses SAP HANA to analyze in real-time data collected from sensors that measure water levels in storm drains

Smart Fleet Analytics

• Real-time control of assets

• Analysis of traffic/ congestion

• Influence driver behavior

• Ensure safety

• Effective fuel management

• Charge for idle/dwell times

State of Indiana (USA)

Big Data solutions improving quality of citizens' lives – State of Indiana chooses SAP HANA platform to help fight infant mortality

Hamburg Port Authority

• Real-time and Efficient freight forwarding

• Efficient container operations

• ETA of trucks

• Efficient parking

• Real-time traffic management

• Smooth traffic flow

• Geo-fencing

Barcelona

Helping tourists feel like citizens! SAP Future Cities helps leverage a city platform that provides personalized and tailored information about the city, shopping, and restaurants, public transport, and access to sights.

Thank-you

henrik.kummel@sap.com