Jürgen Pilz

Database Magic behind 40 Million operations/s

What does it take to scale a database to 40 million operations per second? Learn how Amadeus architected its system to achieve sub-millisecond latency.

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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Matching moments

Exploring the core services of the Couchbase platform
06:17 MIN

Exploring the core services of the Couchbase platform

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Answering questions on Cube's architecture and use cases
07:33 MIN

Answering questions on Cube's architecture and use cases

Making Data Warehouses fast. A developer's story.

Supporting hyperscale workloads with a single database
03:24 MIN

Supporting hyperscale workloads with a single database

Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases

Identifying key use cases for a cloud database
02:03 MIN

Identifying key use cases for a cloud database

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Answering questions on data volume, challenges, and databases
07:21 MIN

Answering questions on data volume, challenges, and databases

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Sofascore's architecture for handling real-time data at scale
03:03 MIN

Sofascore's architecture for handling real-time data at scale

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Q&A on implementation details and technology choices
03:43 MIN

Q&A on implementation details and technology choices

Challenges for omnichannel applications at ALDI: Data distribution and offline capabilities

Comparing Couchbase with MongoDB and discussing migration
04:42 MIN

Comparing Couchbase with MongoDB and discussing migration

Tomorrow's cloud data platforms - fully managed database-as-a-service (DBaaS)

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