Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Enterprise

@yourtwitterhandle | developer.confluent.io
What are the best practices to debug client applications
(producers/consumers in general but also Kafka Streams
applications)?
Starting soon…
STARTING SOOOOON..
Starting sooooon ..
Starting soon…

applications)?
Starting soon…
STARTING SOOOOON..
Starting sooooon ..

Copyright 2021, Conﬂuent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Conﬂuent, Inc.
Streaming Architecture

applications)?

Goal
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Unlocking the Value of
SAP & Conﬂuent for Digital Integration
10

Unlocking Business Value for SAP Intelligent Enterprise
11
SAP Intelligent Enterprise
● Transform business
processes with agility,
innovation, and
responsiveness to market
changes through a
connected ecosystem
● Integrates Emerging
Technologies of AI, ML, IoT to
drive faster and informed
decision making and
automation
● Provides Business Value for
organizations by enabling:
○ Faster Decision Making
○ Enhanced Customer
Experience
○ Operational Excellence
Digital Enterprise
● Reinvent Your Customers'
Experiences
● Build Efﬁciencies in Your
Backend Operations
● Automates digital business
processes in real-time
● Launch New Business
Models and Services
● Leverages Operational
Intelligence with real-time
insights and contextual
decision making
Business Logic
Data
SAP
SAP ERP
Business Technology Platform
App Dev | Automation | Integration | Data & Analytics | AI

DATA IN MOTION
Streaming
Applications
Apache
Flink
Apache
Kafka
DATA AT REST
Application
Layer
Compute
Layer
Storage
Layer
ABAP
Platform
SAP HANA
Database
Fiori UX
The Modern Business Divide
SAP Business Processes
Using Software
Digital Business is Software
Deﬁned

The Paradigm for Data in Motion: Data Streaming
All your data continuously streamed, processed, governed and shared as a product,
making it instantly valuable, usable, and trustworthy everywhere.

Use Case Pattern Applicability for Data Streaming Integration
14
Business Logic
Data
SAP
SAP ERP
Business Logic
Data
SAP
Business Application
Reporting & Analytics
Data
SAP
Data Lakehouse
Interaction Logic
SAP
Digital Channels
Industrial IoT Logic
SAP
Real World Things (IoT)
Connected Services
IoT Logic
Process
Integration
Data
Integration
Thing
Integration
Engagement
Integration

15
Business Logic
Data
SAP
SAP ERP
Business Logic
Data
SAP
Data
SAP
Data Lakehouse
Interaction Logic
SAP
Digital Channels
SAP
Connected Services
IoT Logic
Process
Integration
Data
Integration
Thing
Integration
Engagement
Integration
Data Product
based Integration

16
Business Logic
Data
SAP
SAP ERP
Business Logic
Data
SAP
Data
SAP
Data Lakehouse
Interaction Logic
SAP
Digital Channels
SAP
Connected Services
IoT Logic
Process
Integration
Data
Integration
Thing
Integration
Engagement
Integration
Data Product
based Integration
Data Aggregation
& Real Time Insights

Digital Intelligent Enterprise Use-Cases
17

Manufacturing Process Operational Intelligence Insights
Situational Awareness through Real-Time IoT Data Analysis in Business Process and Customer Order Context
SAP S/4HANA
Production
Orders
Material
Documents
Sales Order
Management
Production
Planning
Delivery
Management
Material
Planning
Production
Order
Fulfillment
Invoicing &
Payment
Collection
Raw Material
Order
Warehouse
Stock Update
Shop Floor Plan
Execution
Finishing
+ Mounting
Shop Floor Part
Tracking
Package for
shipment
Elapsed Time
Tracker
Order Delay
Calculator
Order Prioritization
Optimizer
Identify Bottlenecks
Reports &
Dashboards
● Car Manufacturer manages
Order-to-Delivery process with SAP
S/4HANA
● Once Production Order is released in
SAP, IoT asset tracking for all parts and
car is correlated to customer order
business context using event stream
processing on data streaming platform
● Manufacturing Execution Situational
Awareness enables:
○ Real-time parts tracking to ensure
Build-to-Order options are
delivered at stations in order of cars
on production line, leading to less
mistakes and avoiding manual
retrofitting of parts.
○ Production step execution delays
with cascading effects are
identified and remediated in
real-time, leading to production
capacity and yield optimization.
○ Improved customer experience as
real-time car production
milestones are communicated as
order status notifications to
customers.

Replication
Enrich, Normalise
and Standardise
Data Products
Stream Processor
SAP S4/HANA
Material Master
Material
Documents
Inventory
Locations
Replication
In Store Cash Registers
Sales Receipts
Replenishment
Forecasting
Digital
Channels
In Store
Retail App
Omni-Channel Retail Real-Time Stock Balances
19
Materialized View
for Article and
Location Master
Data
Materialized View
Stock Balances
Stream Processor

Real-Time Data Pipelines for Business Intelligence & Analytics
Enable Faster Decision Making with Business Intelligence using Secured, Governed, Real-Time Data Pipelines for SAP Data
● A CPG company manages all their core business
processes for Sales, Manufacturing, Order
Management, Supply Chain and Finance with SAP
ERP
● Operational management decision making is
supported through their Business Intelligence and
Analytics reporting on top of their Snowflake Cloud
Data Warehouse.
● In order to improve their operational decision
making, they move from point-to-point CDC raw
data ingestion and Batch processing to real-time
streaming data pipelines, to achieve:
○ Improve operational decision making
through data freshness delivered by real-time
streaming data pipelines.
○ Accelerate time to value, by using data
product approach to decouple source system
expertise from centralised data engineering
team.
○ Increase data quality and security by
leveraging centralised data governance
policies on distributed data streaming
platform.
○ Reduced TCO, through centralized platform
replacing unwieldy point-to-point technology
solutions.
○ Reduced TCO, by savings on Snowflake
storage and compute costs by replacing Raw
data layer ingest with data streaming pipeline
that delivers prepared data to Conformed
Layer.
Snowflake on
AWS
Data Cleansing,
Normalising,
Transforming
Streaming Data
Pipeline
Replicatio
n
SAP S/4HANA
Sales Orders
Delivery
Notes
Replicatio
n
Log Data
Click
Streams
Replicatio
n
Customer Experience
Campaign
Data
Ecommerce
Orders
Invoices
CDS
ODP

Unlock SAP Data
Context with
5 fundamental
principles for better
data pipelines
Streaming
Continuously capture, evolve and share high-fidelity
real-time data for all your use cases
Decentralized
Allow teams closest to domain to create and share data
streams for self-service access across your organization
Developer-oriented
Bring software engineering practices to build multiple
models and experiment, test and deploy in an agile manner
Governed
Enable self-service search and discovery while maintaining
security, observability and compliance
Declarative
Build reusable and performant data flows by separating data
topology definition from data processing infrastructure
22

Enable frictionless
access to up-to-date
trustworthy data
products
Share
Reimagine data
streaming everywhere,
on-prem and in every
major public cloud
Stream
Make data in motion
self-service, secure,
compliant and
trustworthy
Govern
Drive greater
data reuse with
always-on stream
processing
Process
Make it easy to
on-ramp and off-ramp
data from existing
systems and apps
Connect
Building Blocks for Modern Data Pipelines

SAP S/4HANA Strategic Cloud Ready Integration Points
24
Database
CDS
ODP
SAP ABAP Platform
Fiori UI
Web APIs
OData
Business
Events
ABAP
SAP S/4HANA Business Service
Core Data Services Behavior Deﬁnition
ABAP RESTful Application Programming
Model
Event-based Integration
with Business Events
Data Integration
via CDS & ODP
or
direct Integration with SAP Datasphere
API Managed Integration
using ODATA Web APIs
Process
Integration
Single
Business
Object
Data
Integration
Tabular
Data
Sets

Notification Events
25
Data Events
● A system sending event messages to notify other systems of a
change in its domain
● There is a marked separation between the logic flow that sends
the event and any logic flow that responds to some reaction to
that event.
● A notification event need not carry much data on it, often just
some id information and a link back to the sender that can be
queried for more information.
● Also referred to as an Event-Carried State Transfer
● A system updates interested applications in such a way that they
don't need to contact the source system in order to do further
work.
● Data may be copied multiple times, should be less of a problem in
an age of abundant storage.
● Data events offer greater resilience, since the recipient systems
can function if the source system is becomes unavailable.
● Reduction in latency, as there's no remote call required to access
event change information.
● No extra load on the source system to satisfy queries from all the
event subscribing systems.
SAP Business Events
Data Streaming Patterns

Database
CDS
ODP
SAP ABAP Platform
Fiori UI
SAP S/4HANA Business Service
Core Data Services
Behavior Deﬁnition
ABAP RESTful Application Programming
Model
OData Web APIs
Business Events
SAP BTP Platform
Customer CSP Account
Private Network Link
SAP Standard Pattern for using Business Events on BTP
26

SAP S/4HANA (Business) Events Integration Options
27
SAP S/4HANA Application
Business Event Handling
Enterprise Event
Enablement
Business Event
Subscription
Subscription Manager
Business Event
Queue
SAP Event Mesh
MQTT / AMQP protocol
Push
Conﬂuent
REST API
INIT Source Connector
for
SAP Business Events
OData
Conﬂuent Source
Connector for JMS
SAP ABAP Platform
SAP BTP
Pull
ASAPIO
Integration Add-on
Connector
Onibex
One Connect
ONIBEX
ZONIBEX_DL
ALE
HTTPS (JSON)

Partnering with the ecosystem to deliver results faster
28
Cloud Technology
Conﬂuent
Professional
Services
System Integrator

Copyright 2020, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
SAP Connectivity Options (Subset) for Confluent Platform / Cloud
29
Connector Name Integration
Style
Source Sink Operation
Management
USP Discussion Points
SAP Integration Suite Kafka Adapter Process
Integration ✓ ✓
SAP (Integration)
Team
SAP official product Further Development, CI
Advantco Kafka Adapter Process
Integration +
Data Integration
✓ ✓
SAP (Integration)
Team
Higher maturity CI Adapter than
SAP native one. Full SR support.
Further Development, CI or SDI.
ASAPIO Integration Add-on Connector Process
Integration +
Data Integration
✓ ✓
SAP Team Native SAP configuration and
management. No-coding for
outbound and IDOC inbound
scenarios. Data enrichment for
SAP events.
ABAP Add-On, REST Proxy, JSON
only, SR limitations.
ONIBEX One Connect Process
Integration +
Data Integration
✓ ✓* SAP Team Native SAP configuration and
management. No-coding for
outbound scenarios. Data
enrichment for SAP events.
Prebuilt SAP Domain models.
Native Kafka client with full SR
support.
ABAP Add-On, Docker based
OneConnect Gateway, Outbound
focussed (✓*), Complete business
monitoring solutions.
INIT SAP Connectors for Kafka Connect Process
Integration +
Data Integration
✓ ✓
Kafka Team Full Integration in Kafka & Kafka
Ops, Native Kafka Connector with
all Confluent extensions (SMTs,
Conversion, SR, C3). Metadata
discovery from SAP BOR for SR.
Pull mechanism on APIs, Kafka and
Confluent Platform native. Support
for many SAP API’s, such as IDOC,
RFC, OData, Business Events, ODP.
Webservice DS..
SAP Datasphere Data Integration
✓ ✓** SAP (Data) Team SAP official product. Kafka &
Confluent Cloud native support in
SAP Datasphere Replication
Flows as well as native SAP ERP /
BW data layer access. Only
certified solution for CDS/ODP
extraction via RFC API.
Kafka support for Datasphere
Replication Flow Source is on the
roadmap (✓**) , as is Metadata
synchronisation with SR.
Qlik Replicate Data Integration
✓
Kafka Team Direct CDC on the database
(log-based).
Direct DB access, SR support.
CONNECT

SAP Datasphere is the foundation for
a business data fabric architecture
Data consumers
SAP and non-SAP data
SAP Datasphere
running in SAP BTP
Security
Access control
Availability
Planning and analytics Intelligent data apps Data science
Applications
On-premises
Data Warehouses
Cloud Data Warehouses
and Lakehouses
Relational
Databases
Unstructured/
Semi-structured Data
Self-service data access | Virtual data products
Data discovery | Business content, data marketplace, recommendations
Orchestration | Data transformation and data ops
Processing and persistency | Warehousing, business semantics
(analytic/relational models), knowledge graph
Data ingestion | Data replication, data federation, real-time data, application integration
Data governance | Metadata management, catalog, lineage, privacy, data quality

SAP chose Conﬂuent among 5 of their ﬁrst Open
Data Partners associated with SAP Datasphere
32

© SAP SE OR AN SAP AFFILIATE COMPANY. ALL RIGHTS
RESERVED.
33
SAP Datasphere and Confluent Data Flow
Q4 2023 – Replicate SAP data to Confluent Kafka with SAP Datasphere (Kafka as target)
Outbound Scenarios
Legend:
SAP Datasphere
Business Content Data Marketplace
Data Catalog
Orchestration
Business Modeling
Monitoring
Data Integration
Data Replication
Replication
Intelligent Data
Applications
Other Data and
Analytics platforms
Consumers
SAP HANA Cloud
Data Processing and Persistency
SAP S/4HANA
SAP BW/4HANA
SAP BW
SAP ECC
SAP HANA
Applications and Analytics
(could include 3rd party systems)
Phase 1

© SAP SE OR AN SAP AFFILIATE COMPANY. ALL RIGHTS
RESERVED.
34
SAP Datasphere and Confluent Data Flow
H1 2024 – Replicate data from Confluent Kafka to SAP Datasphere (Kafka as source)
Outbound Scenarios
Legend:
SAP Datasphere
Business Content Data Marketplace
Data Catalog
Orchestration
Business Modeling
Monitoring
Data Integration
Data Replication
ECC, S/4
BW, Others Replication
Streaming Data
Non-SAP Sources
SAP & Non-SAP
Real Time Data Sources
Consumer Producer
SAP HANA Cloud
Data Processing and Persistency
Intelligent Data
Applications
Other Data and
Analytics platforms
Consumers
Applications and Analytics
(could include 3rd party systems)
Queries/Reporting
Phase 2

Synergy effects for collaboration across CoEs
● Bridge the Gap and moderate the conversations between the Data Streaming CoE teams and
SAP Competence centers at our joint customers.
● Identify SAP A2A interfaces that need to be rethought for a Clean Core S/4HANA
implementation
○ Master Data, Order Management, Production Orders, and Inventory Management are
coming up regularly
● Demonstrate the power of streaming data pipelines to decouple high fidelity SAP managed
data sets for reuse across use-cases with an initial Proof of Concept or Pilot Project
○ Work with your Confluent SE to build confidence with reference use-cases and proven
integration patterns
○ Work with Confluent Technology partners for the appropriate connectivity approach
○ Look at the whole and not the individual parts (Connect - Govern - Enrich - Build - Share)
36

Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Enterprise

Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Enterprise

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