• When and why does DBaaS make sense?
• What the principle & architecture of DBaaS looks like
• Everything from a single source: how and with whom you can use the cloud data platform successfully

Developers always expect databases to work out of the box, but historically that hasn’t been the case. Thanks to performance improvements made to Docker in the last few years and the rise of Kubernetes, running high-performance stateful applications inside containers is now a reality. A few questions have been raised to address this new normal: Why should I do that in the first place? How hard is it? What are the challenges? Is it production ready? These questions and more will be answered during a live demo that will deploy a database and operator, fail nodes, and scale up and down with almost no manual intervention using state-of-the-art technology.

We're searching across vast amounts of data every day. None of that data is stored in a single system - due to its sheer size and to prevent data loss on node outages. But how does a distributed search look like? How is data aggregated and calculated that is placed on dozens of nodes? How is coordination working in a good state of a cluster? What happens if things go sideways and how does recovery within a cluster work? Using Elasticsearch as an example this talk shows a fair of examples of distributed communication, computation, storage and statefulness in order to give you a glimpse what is done to keep data safe and available, no matter if running on bare-metal, on k8s or virtualized instances.

GraphQL exposes application data as a graph which can introduce challenges if your backend isn't graph-ready (think slow JOINs as a result of nested GraphQL queries and the dreaded n+1 query problem). The Neo4j GraphQL library enables developers to build GraphQL APIs backed by a native graph database using only GraphQL type definitions. In this talk we'll see how to build a GraphQL API without writing any resolvers, add custom logic, and deploy to the cloud.