Kubernetes for Data Engineering

This repository contains the necessary configuration files and DAGs (Directed Acyclic Graphs) for setting up a robust data engineering environment using Kubernetes and Apache Airflow. It includes the setup for the Kubernetes Dashboard, which provides a user-friendly web interface for managing Kubernetes clusters, and Apache Airflow, a platform to programmatically author, schedule, and monitor workflows.

Repository Structure

The repository is organized as follows:

.
├── dags
│   ├── fetch_and_preview.py
│   └── hello.py
└── k8s
    ├── dashboard-adminuser.yaml
    ├── dashboard-clusterrole.yaml
    ├── dashboard-secret.yaml
    ├── recommended-dashboard.yaml
    └── values.yaml

DAGs

• fetch_and_preview.py: A DAG for fetching data and providing a preview.
• hello.py: A simple example DAG to demonstrate basic Airflow concepts.

Kubernetes (k8s) Configuration

• dashboard-adminuser.yaml: YAML file for setting up an admin user for the Kubernetes Dashboard.
• dashboard-clusterrole.yaml: YAML file defining the cluster role for the Kubernetes Dashboard.
• dashboard-secret.yaml: YAML file for managing secrets used by the Kubernetes Dashboard.
• recommended-dashboard.yaml: YAML file for deploying the recommended Kubernetes Dashboard setup.
• values.yaml: YAML file containing values for customizing the Kubernetes setup.

Getting Started

Prerequisites

• A Kubernetes cluster
• kubectl installed and configured
• Helm (optional, but recommended for managing Kubernetes applications)

Setup

1. Deploy the Kubernetes Dashboard:

   To deploy the Kubernetes Dashboard, apply the YAML files in the k8s directory:

   kubectl apply -f k8s/

   This will set up the Kubernetes Dashboard with the necessary roles and permissions.

2. Accessing the Kubernetes Dashboard:

   To access the Dashboard, you may need to start a proxy server:

   kubectl proxy

   Then, access the Dashboard at: http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/.

   Use the token generated for the admin user to log in (see dashboard-secret.yaml).

3. Deploy Apache Airflow:

   You can deploy Apache Airflow using Helm or by applying custom YAML files. For Helm:

   helm repo add apache-airflow https://airflow.apache.org
   helm install airflow apache-airflow/airflow -f k8s/values.yaml

   This will deploy Airflow with the settings defined in values.yaml.

4. Adding DAGs to Airflow:

   Copy your DAG files (e.g., fetch_and_preview.py, hello.py) into the DAGs folder of your Airflow deployment. The method of copying depends on your Airflow setup (e.g., using Persistent Volume, Git-sync).

Usage

• Kubernetes Dashboard: Use the Dashboard to monitor and manage the Kubernetes cluster.
• Apache Airflow: Access the Airflow web UI to manage, schedule, and monitor workflows.

Video

For a complete walkthrough and practical demonstration, check out the video here: