Jürgen Pilz
Database Magic behind 40 Million operations/s
#1about 3 minutes
Understanding the scale of Amadeus's travel booking operations
Amadeus processes billions of passenger bookings, requiring a database architecture that can scale to handle over 40 million operations per second.
#2about 4 minutes
Introducing Couchbase as a unified multi-model database
Couchbase consolidates multiple database functionalities like caching, search, and analytics into a single platform using a memory-first architecture and a SQL-like language for JSON.
#3about 3 minutes
Scaling and ensuring high availability with Couchbase architecture
Couchbase achieves linear scalability and high availability by allowing independent scaling of services and using automatic data replication for seamless failover.
#4about 2 minutes
Managing flight catalog and inventory with a flexible schema
Amadeus uses Couchbase's flexible JSON document model to aggregate diverse data like flights, fares, and insurance for real-time inventory management.
#5about 3 minutes
Building a scalable Customer 360 platform
Amadeus replaced a traditional relational database with Couchbase to achieve the scalability and 100% availability needed for its Customer 360 solution.
#6about 2 minutes
Implementing a global hybrid-cloud database architecture
Amadeus deploys Couchbase across global on-premise and cloud data centers using cross-data-center replication and Kubernetes for management.
#7about 3 minutes
Extending data to mobile devices with Couchbase Lite
Couchbase Lite enables offline-first mobile applications with peer-to-peer synchronization, allowing data access and updates even without an internet connection.
#8about 4 minutes
Answering technical questions about Couchbase features
The Q&A covers topics like migration from CouchDB, referential integrity, time-series data handling, GDPR compliance, and multi-master setups.
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Answering questions on data volume, challenges, and databases
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Q&A on implementation details and technology choices
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04:42 MIN
Comparing Couchbase with MongoDB and discussing migration
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Amazon's early monolith and database scaling challenges
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