Generali connection platform_full
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The document discusses Generali's journey towards building a real-time data platform called the Connection Platform (CoPa) using Kafka and related technologies. It describes Generali's starting position facing regulatory pressure and changing customer expectations. It then outlines Generali's initial strategy, a high-level solution sketch for CoPa, and lessons learned from the project so far. CoPa is now running in production with over 300 topics across 5 environments to power new projects and applications.
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Generali connection platform_full
- 1. Public Department: Country: © Generali Switzerland Generali Connection Platform Under 10 seconds from source to target Christian Nicoll, Director of Platform Engineering & Operations, Generali Christopher Knauf, Director of Sales, Attunity Kai Waehner, Technology Evangelist, Confluent Operations & IT Switzerland
- 2. Public © Generali Switzerland 12.12.18 Your Speakers today Christopher Knauf Director of Sales Attunity Kai Waehner Technology Evangelist Confluent Christian Nicoll, Director of Platform Engineering & Operations Generali
- 3. Public © Generali Switzerland 12.12.18
- 4. Public 4© 2017 Attunity #1 provider of change data capture (CDC) Support most sources with best performance and least impact #1 cloud database migration technology Already moved over 85,000 databases to Cloud platforms like AWS #1 in ease of use Designed to accelerate deployments by data architects and DBAs instead of developers The leading platform for delivering data efficiently and in real-time to data lake, streaming and cloud architectures Attunity
- 5. Public 5© 2017 Attunity Pre-packaged automation of complex tasks Modern user experience Zero source footprint Change data capture (CDC) Stream live updates Optimized for high- performance movement All major platforms DB | DW | Hadoop | Legacy On Premises | Cloud SAP | Mainframe Simplified Real-Time Universal DATA DELIVERY WITH ATTUNITY REPLICATE
- 6. Public 6© 2017 Attunity ATTUNITY REPLICATE ARCHITECTURE TRANSFER IN-MEMORY FILTER HADOOP RDBMS DATA WAREHOUSE FILES MAINFRAME TRANSFORM PERSISTENT STORE CDC BATCH INCREMENTAL BATCH HADOOP RDBMS DATA WAREHOUSE STREAMING FILES
- 7. Public 7© 2017 Attunity 1) Data Lake continuous ingestion & pipeline automation 2) Streaming database changes to Kafka, Azure Event Hub, etc. 3) Cloud database migration to AWS/Azure 4) SAP data availability for analytics 5) Mainframe data delivery and query offload MAINFRAMEEDWSAP RDBMS DATA LAKES STREAMING CLOUD ATTUNITY USE CASES
- 8. Public 8© 2017 Attunity
- 9. Public Founded by the creators of Apache Kafka Technology Developed while at LinkedIn Largest Contributor and tester of Apache Kafka ● Founded in 2014 ● > 450 Employees purely focused on Apache Kafka ● Transacting in 20 countries ● Hundreds of enterprise subscription customers ● Commercial entities in US, UK, Germany, Australia Organizers of the largest Kafka community event Hundreds of enterprise subscription customers across industries
- 10. Public 10C K O 2 0 1 8 J U L Y Improve Customer Experience (CX) Increase Revenue (make money) Business Value Decrease Costs (save money) Core Business Platform Increase Operational Efficiency Migrate to Cloud Mitigate Risk (protect money) Fraud Detection IoT sensor ingestion Digital replatforming/ Mainframe Offload Connected Car: Navigation & improved in-car experience: Audi Customer 360 Simplifying Omni-channel Retail at Scale: Target Faster transactional processing / analysis incl. Machine Learning / AI Mainframe Offload: RBC Microservices Architecture Online Fraud Detection Online Security (syslog, log aggregation, Splunk replacement) Middleware replacement Regulatory Application Modernization: Multiple Examples Website / Core Operations (Central Nervous System) The [Silicon Valley] Digital Natives; LinkedIn, Netflix, Uber, Yelp... Predictive Maintenance: Audi Streaming Platform in a regulated environment (e.g. Electronic Medical Records): Celmatix Real-time app updates Real Time Streaming Platform for Communications and Beyond: Capital One Developer Velocity - Building Stateful Financial Applications with Kafka Streams: Funding Circle Detect Fraud & Prevent Fraud in Real Time: PayPal Kafka as a Service - A Tale of Security and Multi-Tenancy: Apple $↑ $↓ $ Example Case Studies (of many)Digital Transformation 10 business use case Strategic Driver 20 business use case Example Case Studies
- 11. Public Confluent Delivers a Mission-Critical Streaming Platform
- 12. Public Best-of-breed Platforms, Partners and Services for Multi-cloud Streams Private Cloud Deploy on bare-metal, VMs, containers or Kubernetes in your datacenter with Confluent Platform Public Cloud Implement self-managed in the public cloud or adopt a fully managed service with Confluent Cloud Hybrid Cloud Build a persistent bridge between datacenter and cloud with Confluent Replicator Confluent Replicator VM SELF MANAGED FULLY MANAGED
- 13. Public We work with organizations to increase their Streaming Maturity Value Maturity (Investment & time) 2 Early Production Streaming Pub + Sub Store Process 5 Central Nervous System 1 Streaming Awareness & Pilot Pre-Streaming 4 Global Streaming 3 Mission Critical Integrated Streaming Projects Platform
- 15. Public © Generali Switzerland Starting position Regulatory pressure Stagnating economy Changes in customer expectations Brand differentiation Digitization & modernization Challenging market environment
- 16. Public © Generali Switzerland 12/12/2018 Sleeping beauty https://vignette.wikia.nocookie.net/grimmbilder/images/b/b5/Dornroeschen_Paul_Hey_Jahr_unbekannt.jpg/revision/latest?cb=20130516132113&path-prefix=de
- 17. Public © Generali Switzerland 12/12/2018 Changes in customer expectations
- 18. Public © Generali Switzerland 12/12/2018 How to connect both worlds? https://stock.adobe.com/de/images/grey-unfinished-bridge/97597672
- 19. Public © Generali Switzerland 12/12/2018 How things looked in January 2017 Adopted strategy: • Aiming for 2 • Maybe some 1 • But not 3
- 20. Public © Generali Switzerland 12/12/2018 High-level solution sketch Connection Platform (CoPa) Kafka / Confluent CDC Attunity Replicate Integration Model Landing Zone Service Layer CA API Gateway Salesforce CI / CD RedHat OpenShift Shipping Model RedHat OpenShift Core Systems IoT OpenShift Customer Portal … … Data processing with dockerized Kafka Streams Microservices in Openshift
- 21. Public © Generali Switzerland 12/12/2018 Review and recommendations Challenges • Assembling the various parts to one platform • Run the CoPa on premise • Build the CoPa within one year • Working with high- level requirements Good practices • People are key • Automation • Weekly maintenance drain as resilience- fitness check • Establish partnerships with other companies Lessons learned • Don’t neglect IT infrastructure • Think early about processes, governance & security • Expect setbacks • Be prepared to build an emergent architecture
- 22. Public © Generali Switzerland 12/12/2018 Full speed towards the future > 300 topics / environment 5 environments are up-and-running First productive workload since September Next projects are waiting Scaling CoPa
- 23. Public Thank You. Contacts: © Generali Switzerland 12.12.18 Christopher Knauf Director of Sales Attunity Christopher.Knauf@attunity.com Kai Waehner Technology Evangelist Confluent kai.waehner@confluent.io Christian Nicoll, Director of Platform Engineering & Operations Generali christian.nicoll@generali.com