Progress For Big Data In Kubernetes
summary
Kubernetes is now ubiquitous (CNCF finds 60–70% of large companies in production) yet it only handles compute; persistent state breaks container fluidity. The concrete solution is a data platform that exposes files tables and streams via a sidecar container driver so existing containers need no code changes, supports Helm deployment, provides a global namespace across clouds and on‑prem, and enables multi‑site replication — examples include manufacturing edge clusters and a bank using jurisdictional partitions.
AI-written summary · about
chapters (12)
- 0:00Introduction and speaker
- 1:26Why Kubernetes adoption
- 3:17What is Kubernetes
- 5:44Containers vs VMs
- 8:16Containers as cattle
- 10:45Kubernetes orchestration
- 14:44Challenge of state in containers
- 15:52Services and streaming
- 18:28Need for data platform
- 21:46Data orchestration benefits
- 23:19Unifying cloud resources
- 26:12Further resources
related talks
41mBig News for Big Data in Kubernetes #bbuzz
24mKubernetes & Data Engineering
24mDynamic Big Data in the Cloud with Kubernetes
35mModern Big Data Pipelines over Kubernetes [I]
05mMaking the Impossible Possible with Kubernetes
25mPostgres on Kubernetes for the Reluctant DBA
47mData Mesh in Kubernetes
07mData Gravity and Kubernetes: Managing Large-Scale Data Ingest With Minimal Latency
