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Authors: Petter Sveum
2023-04-21

tldr - powered by Generative AI

The presentation discusses the challenges of data protection and application recovery in cloud and Kubernetes architectures and proposes an autonomous data management platform as a solution.
  • More customers are building critical infrastructure into Kubernetes, but struggle with data protection and recovery.
  • Critical applications require persistent storage and disaster recovery orchestration.
  • An autonomous data management platform should be cloud-optimized, API-enabled, microservices containerized, elastic, multi-cloud, and multi-tenant.
  • The platform should deliver advanced functionalities like automated capacity management, self-optimization, recovery of service, resiliency of service, and end-to-end security.
  • The platform should apply a set of criteria for protecting workloads and ensuring security and predictability for recovery.
  • The platform should provide an outcome where data is protected end-to-end, resiliency can be managed, tested, and validated, and there is optimized and efficient usage of infrastructure.
Authors: Chris Milsted, Gabriele Bartolini
2022-10-27

In this talk we are going to focus on the newly open sourced Cloud Native PostgreSQL operator. From work in the Data on Kubernetes community, we are seeing databases becoming first class citizens in our kubernetes clusters. This talk is going to look at how to combine the cloudnative-pg operator with a CSI-plugin, and how to leverage these two components to deliver databases which are as fast and resilient as their non-kubernetes equivalents. Wondering how to architect for a zero or low RTO and what the effect of replication has on recovery time for your database? Wondering how to use an object store as well to deliver point in time backups to protect from cyber attacks? In this talk we are going to answer these questions and more mapping to the patterns in the cloud native disaster recovery whitepaper and the data protection whitepaper. Like all good talks we will also have a live demo of this all in action. The time of Data on Kubernetes is now, come and find out how.
Authors: Henrik Høegh
2022-05-20

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The presentation discusses how Lunar bank achieved the capability of doing a failover using Githubs and moved from canary deployments to canary clusters. The main focus is on the tech stack used and the challenges faced during the process.
  • Lunar bank moved from canary deployments to canary clusters to meet the needs of their customers who rely on them to move quickly and provide new features in a highly reliable manner.
  • The production clusters were made truly disposable by deeply integrating with the infrastructure provider, writing new custom operators, and moving most state out of the cluster.
  • The company achieved the capability of doing a failover using Githubs, which was complex and required a lot of work.
  • The tech stack used included Kubernetes, Git Ups, Flux, AWS, S3 bucket, RabbitMQ, and external DNS.
  • The challenges faced included merge complexity in the Github repo, stalling of new deployments during the exercise, and discomfort among employees due to the complexity of the process.
Authors: Shyam Ranganathan, Orit Wasserman
2021-10-13

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The presentation discusses disaster recovery of stateful applications in a multi-cluster environment using replication capable storage systems like Ceph/Rook.
  • Disaster recovery is important to ensure business continuity in case of data center loss.
  • Regional disaster recovery involves two separate remote sites with high network latency and two separate Kubernetes clusters.
  • Replication capable storage systems like Ceph/Rook can be leveraged to provide disaster recovery of workloads across clusters.
  • A multi-cluster control plane is required to enable one-click disaster recovery solution for stateful workloads.
  • Volume replication and volume application class are added to the standard CSI API to enhance capabilities.
  • Dynamic provisioning requires creating a matching PV in the recovery site and connecting it to the replicated volume.
  • Multi-cluster management requires equivalent cluster configurations and deployment of custom resources and operators on all clusters.