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Agile Data Integration
How is it Possible?
Meetup, 27th of March 2018
Thomas Peter (Generali Switzerland)
Yves Brise (Innovation Process Technology)
Disclaimer
The following presentation is for general information,
education and discussion purposes only. Views or
opinions expressed, whether oral or in writing do not
necessarily reflect those of Generali or ipt nor do they
constitute legal or professional advice. -> But it rocks!
2
A new Connection Platform for Generali CH
• GCH starts conceiving and designing the new integration and application platform
• GCH starts building new platform MVP in collaboration with IPT (in about 9 months)
Embedded in GCH Enterprise Cloud-CRM program, business applications (e.g. integration
paths for Cloud-CRM as well as other new applications) are delivered by third party providers
MVP, MVP, MVP: just do it and…
Platform MVP delivered to program on the 15th march 18
3
March 17
March 18
June 17
Innovation Process Technology
• IT Service Provider
• CH based, ca. 115 employees
• Strategic integration partner for Generali
• Premier partner of Confluent
• A great place to work: www.ipt.ch
Data-Driven Business Process Digitalization Cyber Security Agile Organizations
4
The Vision of Data-Driven Business
«[…] the means by which an organisation seeks to
maximise the efficiency with which it plans, collects,
organises, uses, controls, stores, disseminates, and
disposes of its data, and through which it ensures
that the value of that data is identified and exploited
to the maximum extent possible.»
Adapted from: Oracle, Information Management and Big Data – A Reference Architecture, September 20145
Key Elements to Become More Data-Driven
6
„Data First“
Technology
Governance
Friendliness
Project
Enablement
Agile Data
Integration
Design for
Scalability
Part I
Kafka
⏤
Cornerstone of Integration
Openshift, Docker, and a Green Button
CoPa Physical Technology Stack
Physical Infrastructure
Operating System
Virtualization
OpenShift
Docker
API GW CDC Confluent
Data
Store
Spring Boot
CI/CD(Infrastructure,
ProjecInitilizer,
Customization)
App
App
App
App App
App
App
App
• Infastructure provider takes
care of bottom layers
• Openshift / Kubernetes /
Docker as Container layer
• Spring Boot as application
framework
• Functionality provided as
service in the platform: e.g.
API GW, Data Store, CDC,
Kafka
• Top Layers DevOps enabled
8
CoPa Logical Layers
Ingestion & Delivery
Process & Persistence
Service
Access
Client
SA AT
Res
Res
Res
CDC
Res
KS KS
Proxy GW Proxy GW
Connect
9
• Sources and targets are
served through
ingestion & delivery
layer
• ‘External’ clients are
served through service
layer implementations
(Resources)
• Security is guaranteed
on the access layer
• Identities are translated
on the service layer
Getting In and Out of CoPa • One Openshift instance hosts all
production stages (DEVL, TEST, …)
• Separation is guaranteed through
multitenant networking plugin
• Kafka is not exposed to the outside of
Openshift cluster (yet)
• Access from/to outside solely through
Confluent REST proxy
• Kafka clients are authenticated via
client certificates
• If outside access to Kafka is needed,
use 3rd party networking plugin (e.g.
Calico, Contiv,…) that allows BGP
• Network performance no bottleneck
(yet)
10
The Green Button Deploys
11
Part II
Integration Model
⏤
Cornerstone of Governance
Enable Enterprise Architecture to govern data models
while remaining flexible and consumer-driven
Master-Slave Data Flow in System Integration
Master
View A View B
Slave A Slave B
Query
[push]
Query
[pull]
maintains
Change commandChange command
Looks like CQRS.
But how to build the view?
13
Journey Towards Event Streaming
«Process data as it has changed»
«Process change data events»
Listen &
Copy
1:1 Table
Replication
Batch
Processing
Domain based
Table
Batch
Processing
Optimized
Table
Replication:
ChangedData
Processing
Streaming:
ChangeEvent
Processing
Data Source
in Core
Data is
changed
Data is
changed
Data Source
in Core
Ingest
Event
Technical
Event Journal
Process
Event
Domain based
Event Journal
Process
Event
Optimized
Event Journal
Consume
Data
Consume
Data or Event
14
Governance Friendliness
Landing
Zone
Trans
form
Integration
Model
Trans
form
Shipping
Model
CoPa
1:1 from source As requested by consumerOwned by EA
Slave(s)
customized,diversified
unifiedview
Point of governance:
Structure & Content (AVRO & Schema Registry)
Access (ACL & TLS Client Auth)
No new data master - no new truth
Master(s)
legacy,diversified
15
The Integration Model is Document-Based
11 April 2018
K
V
K
V
K
V
K
V
K
V
K
V
K
V
im.party - COMPACT
Unique Partner
Aggregate identifier
Partyinformation
Physicaladdress
Contactaddress
PhonecontactPoint
Emailcontactpoint
Party header
Partyheader
Party record state
Address record state
Contact point record state
16
Main drivers of this design decision
• Coherent contexts
• Join-once-shredder-often
• Dynamic and expressive
• No re-keying needed
«Party» as an example
Part III
Patterns for the Kafka Cluster
⏤
Cornerstone of Solution Design
How do integration and application patterns contribute?
Application & Integration Patterns for Solution Design
18
CoPa
Event-driven
Business
Objects
Event
Sourcing
Event
Distribution
Event
Streaming
Name
Alias
Description
Application or Integration
In scope of integration model
Cleanup policy
New source of truth
Naming convention
Data format
Key format
Schema Registry schemes
Keying schemes
Consumer / Producer schemes
Delivery semantics
Event-Driven BOs Provides Consumer-Driven Data Views
Event-driven Business Objects: data change events flow from a master to one or many slaves
in order to be queried by them in the form appropriate to them
19
Event-Distribution Ensures Transactional Integrity
Event-Distribution: data change commands flow from a slave to a master in order to be applied there
or requests flow from a client to a server in order to be executed
20
Event-Streaming Handles ‚Big Data‘ Loads
Event-Streaming: events flow from producers to consumers in order to be analyzed and
processed along a given time-window
21
Event-Sourcing Provides the Complete Change History
Event-Sourcing: data change events are stored in the sequence they occur in order to be able
to derive any state in time
22
In order to succeed,
technology,
governance and
solution design
need to scale well and
become teammates!
Anhang
Kafka Meetup – 27th of March 2018
26
6:00pm Doors open
6:10pm – KSQL - An Open Source Streaming Engine for Apache Kafka
6:45pm Kai Waehner (Confluent)
6:45pm – Data Integration with Kafka on Openshift
7:20pm Thomas Peter (Generali)
Yves Brise (IPT)
7:20pm – Networking, Apéro and Drinks
8:30pm

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Agile Data Integration: How is it possible?

  • 1. Agile Data Integration How is it Possible? Meetup, 27th of March 2018 Thomas Peter (Generali Switzerland) Yves Brise (Innovation Process Technology)
  • 2. Disclaimer The following presentation is for general information, education and discussion purposes only. Views or opinions expressed, whether oral or in writing do not necessarily reflect those of Generali or ipt nor do they constitute legal or professional advice. -> But it rocks! 2
  • 3. A new Connection Platform for Generali CH • GCH starts conceiving and designing the new integration and application platform • GCH starts building new platform MVP in collaboration with IPT (in about 9 months) Embedded in GCH Enterprise Cloud-CRM program, business applications (e.g. integration paths for Cloud-CRM as well as other new applications) are delivered by third party providers MVP, MVP, MVP: just do it and… Platform MVP delivered to program on the 15th march 18 3 March 17 March 18 June 17
  • 4. Innovation Process Technology • IT Service Provider • CH based, ca. 115 employees • Strategic integration partner for Generali • Premier partner of Confluent • A great place to work: www.ipt.ch Data-Driven Business Process Digitalization Cyber Security Agile Organizations 4
  • 5. The Vision of Data-Driven Business «[…] the means by which an organisation seeks to maximise the efficiency with which it plans, collects, organises, uses, controls, stores, disseminates, and disposes of its data, and through which it ensures that the value of that data is identified and exploited to the maximum extent possible.» Adapted from: Oracle, Information Management and Big Data – A Reference Architecture, September 20145
  • 6. Key Elements to Become More Data-Driven 6 „Data First“ Technology Governance Friendliness Project Enablement Agile Data Integration Design for Scalability
  • 7. Part I Kafka ⏤ Cornerstone of Integration Openshift, Docker, and a Green Button
  • 8. CoPa Physical Technology Stack Physical Infrastructure Operating System Virtualization OpenShift Docker API GW CDC Confluent Data Store Spring Boot CI/CD(Infrastructure, ProjecInitilizer, Customization) App App App App App App App App • Infastructure provider takes care of bottom layers • Openshift / Kubernetes / Docker as Container layer • Spring Boot as application framework • Functionality provided as service in the platform: e.g. API GW, Data Store, CDC, Kafka • Top Layers DevOps enabled 8
  • 9. CoPa Logical Layers Ingestion & Delivery Process & Persistence Service Access Client SA AT Res Res Res CDC Res KS KS Proxy GW Proxy GW Connect 9 • Sources and targets are served through ingestion & delivery layer • ‘External’ clients are served through service layer implementations (Resources) • Security is guaranteed on the access layer • Identities are translated on the service layer
  • 10. Getting In and Out of CoPa • One Openshift instance hosts all production stages (DEVL, TEST, …) • Separation is guaranteed through multitenant networking plugin • Kafka is not exposed to the outside of Openshift cluster (yet) • Access from/to outside solely through Confluent REST proxy • Kafka clients are authenticated via client certificates • If outside access to Kafka is needed, use 3rd party networking plugin (e.g. Calico, Contiv,…) that allows BGP • Network performance no bottleneck (yet) 10
  • 11. The Green Button Deploys 11
  • 12. Part II Integration Model ⏤ Cornerstone of Governance Enable Enterprise Architecture to govern data models while remaining flexible and consumer-driven
  • 13. Master-Slave Data Flow in System Integration Master View A View B Slave A Slave B Query [push] Query [pull] maintains Change commandChange command Looks like CQRS. But how to build the view? 13
  • 14. Journey Towards Event Streaming «Process data as it has changed» «Process change data events» Listen & Copy 1:1 Table Replication Batch Processing Domain based Table Batch Processing Optimized Table Replication: ChangedData Processing Streaming: ChangeEvent Processing Data Source in Core Data is changed Data is changed Data Source in Core Ingest Event Technical Event Journal Process Event Domain based Event Journal Process Event Optimized Event Journal Consume Data Consume Data or Event 14
  • 15. Governance Friendliness Landing Zone Trans form Integration Model Trans form Shipping Model CoPa 1:1 from source As requested by consumerOwned by EA Slave(s) customized,diversified unifiedview Point of governance: Structure & Content (AVRO & Schema Registry) Access (ACL & TLS Client Auth) No new data master - no new truth Master(s) legacy,diversified 15
  • 16. The Integration Model is Document-Based 11 April 2018 K V K V K V K V K V K V K V im.party - COMPACT Unique Partner Aggregate identifier Partyinformation Physicaladdress Contactaddress PhonecontactPoint Emailcontactpoint Party header Partyheader Party record state Address record state Contact point record state 16 Main drivers of this design decision • Coherent contexts • Join-once-shredder-often • Dynamic and expressive • No re-keying needed «Party» as an example
  • 17. Part III Patterns for the Kafka Cluster ⏤ Cornerstone of Solution Design How do integration and application patterns contribute?
  • 18. Application & Integration Patterns for Solution Design 18 CoPa Event-driven Business Objects Event Sourcing Event Distribution Event Streaming Name Alias Description Application or Integration In scope of integration model Cleanup policy New source of truth Naming convention Data format Key format Schema Registry schemes Keying schemes Consumer / Producer schemes Delivery semantics
  • 19. Event-Driven BOs Provides Consumer-Driven Data Views Event-driven Business Objects: data change events flow from a master to one or many slaves in order to be queried by them in the form appropriate to them 19
  • 20. Event-Distribution Ensures Transactional Integrity Event-Distribution: data change commands flow from a slave to a master in order to be applied there or requests flow from a client to a server in order to be executed 20
  • 21. Event-Streaming Handles ‚Big Data‘ Loads Event-Streaming: events flow from producers to consumers in order to be analyzed and processed along a given time-window 21
  • 22. Event-Sourcing Provides the Complete Change History Event-Sourcing: data change events are stored in the sequence they occur in order to be able to derive any state in time 22
  • 23. In order to succeed, technology, governance and solution design need to scale well and become teammates!
  • 25. Kafka Meetup – 27th of March 2018 26 6:00pm Doors open 6:10pm – KSQL - An Open Source Streaming Engine for Apache Kafka 6:45pm Kai Waehner (Confluent) 6:45pm – Data Integration with Kafka on Openshift 7:20pm Thomas Peter (Generali) Yves Brise (IPT) 7:20pm – Networking, Apéro and Drinks 8:30pm