Integrating Apache Beam SDK Harness as Sidecars: A Proven Architecture with Codebase

Author(s): Mahmudur R Manna

Originally published on Towards AI.

Deploying Python Jobs on Flink Servers in Kubernetes with MinIOSource: Image by Author using AI

This member-only story is on us. Upgrade to access all of Medium.

Link for Non-Members:

Running Apache Beam on a Flink server with a Portable Job Server within Kubernetes offers an effective architecture for an agnostic ML workflow. While extensive documentation exists for Apache Beam and Apache Flink separately, comprehensive guides that integrate them into a cohesive, end-to-end pipeline — complete with code examples and detailed instructions — are scarce even tools like Copilot and ChatGPT o1 fell short. This scarcity makes it difficult to set up and run such configurations locally using Minikube, especially when simulating a production environment.

This guide provides a step-by-step walkthrough for configuring an agnostic ML workflow using Apache Beam and Flink on Minikube with the Python SDK. By following this guide, you’ll bridge the documentation gap and streamline your setup process, enabling you to effectively simulate and deploy your ML workflows in a Kubernetes environment.

Apache Beam is an exceptional tool for agnostic machine learning workflows. It allows you to write jobs in Python, Java, Go, or Scala and run them across diverse runners like Spark or Flink with the same language SDK Harness. This flexibility is… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI