# Kubernetes
> 23 posts about Kubernetes, summarised, each linking to the original.

## Articles

### [The Journey of Deploying Apache Airflow at Grab](https://yomu.fyi/post/the-journey-of-deploying-apache-airflow-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Chandulal Kavar
- Published: Jul 14, 2020

Engineering and data teams across Grab originally operated independent Apache Airflow instances, causing duplicate maintenance overhead and frequent job failures around scaling, logging, and dependency management. To resolve this, a dedicated team developed a centralized orchestration platform that runs isolated, containerized Airflow instances per team on Amazon EKS. The platform categorizes deployments into three size tiers and provisions dedicated Redis brokers, RDS metadata stores, and Vault secret sidecars using Terraform and custom Helm charts. Teams customize container images using shared GitLab CI/CD templates, while worker scaling is handled via Kubernetes Horizontal Pod Autoscalers. Today, the platform runs roughly 20 Airflow instances executing between 1,000 and 60,000 daily jobs per instance.


### [Plumbing At Scale](https://yomu.fyi/post/plumbing-at-scale.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Karan Kamath
- Published: Jan 6, 2020

Grab's backend services process terabytes of data ingress per hour, generating recurring needs for stream transformations, joins, and time-windowed aggregations across diverse workloads. To support these asynchronous processing patterns across their Go ecosystem, the Coban team developed a managed, NoOps event sourcing and stream processing platform. The architecture packages stateless processing pipelines as Kubernetes deployments on AWS, polling Kafka event logs and using ScyllaDB as a shared metastore for stateful needs like deduplication and windowing. Stream processing pods combine ingestion triggers, a worker pool runtime, and user-provided domain logic plugins with customizable failure handling. This infrastructure scales to handle over 300 billion events weekly while maintaining workload isolation and elastic autoscaling.


### [Catwalk: Serving Machine Learning Models at Scale](https://yomu.fyi/post/catwalk-serving-machine-learning-models-at-scale.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Nutdanai Phansooksai
- Published: Jul 2, 2019

As machine learning adoption expanded at Grab, individual teams created fragmented model serving solutions that duplicated engineering effort and required data scientists to handle underlying infrastructure. To resolve these inefficiencies, Grab developed Catwalk, a self-service machine learning model serving platform. The system runs TensorFlow Serving containers across a managed Kubernetes cluster integrated with Grab's observability stack. Data scientists deploy or update models simply by saving files using the tf.saved\_model API to dedicated Amazon S3 buckets, while Kubernetes automates orchestration, ingress routing, and pod autoscaling. Catwalk abstracts server management away from data scientists, shortens deployment timelines, and provides high availability during model version rollouts.


[Newer posts](https://yomu.fyi/topic/kubernetes.md)