We’re excited to announce the migration of Google’s Spark Operator to the Kubeflow Spark Operator, marking the launch of a significant addition to the Kubeflow ecosystem. The Kubeflow Spark Operator simplifies the deployment and management of Apache Spark applications on Kubernetes. This announcement isn’t just about a new piece of technology, it’s about building a stronger, open-governed, and more collaborative community around Spark on Kubernetes.

The Journey to Kubeflow Spark Operator

The journey of the Kubeflow Spark Operator began with Google Cloud Platform’s Spark on Kubernetes Operator (https://cloud.google.com/blog/products/data-analytics/data-analytics-meet-containers-kubernetes-operator-for-apache-spark-now-in-beta). With over 2.3k stars and 1.3k forks on GitHub, this project laid the foundation for a robust Spark on Kubernetes experience, enabling users to deploy Spark workloads seamlessly across Kubernetes clusters.

Growth and innovation require not just code but also community. Acknowledging the resource and time limitations faced by Google Cloud’s original maintainers, Kubeflow has taken up the mantle.This transition is not merely administrative but a strategic move towards fostering a vibrant, diverse, and more actively engaged community.

Why Kubeflow?

  • Enhanced Community Engagement: Transitioning to Kubeflow opens the door to a broader developer base, encouraging contributions and collaboration. Since Kubeflow is a CNCF incubating project this transition will help consolidate Cloud Native and Spark communities to work more closely to build robust infrastructure to run Spark applications on Kubernetes.

  • Stronger Governance: Kubeflow’s governance model provides a structured environment for decision-making and project management, ensuring sustainable growth for the Spark Operator.

  • Unified Ecosystem: By bringing the Spark Operator under the Kubeflow umbrella, we’re not just merging projects; we’re building a cohesive ecosystem that enhances the Spark on Kubernetes experience.

  • Integration with AI/ML: Kubeflow provides several components to address many stages of the AI/ML lifecycle. The Spark distributed data processing capabilities are a natural expansion, allowing the Spark community to closely collaborate and better integrate within the end-to-end ML lifecycle.

What’s Next?

We are dedicated to not just maintaining but enhancing the Kubeflow Spark Operator for the long term. Here’s what you can look forward to:

  • Upcoming roadmap: As part of the first release, we aim to update the documentation with references to Kubeflow, address GitHub workflow issues, and update the container registry with Kubeflow, along with any other critical issues.

  • Ongoing Support and Enhancements: At the time of migration to the Kubeflow repository, the repository comprised 450+ issues and 60+ pull requests. We kindly request contributors to rebase their code and update the PR with a comment indicating its continued relevance. As for open issues, they will be considered for resolution as the broader community and contributors engage in upcoming releases.The operator will continue to evolve, incorporating new features and improvements to stay at the forefront of Kubernetes deployments.

  • Rich Community Resources: From detailed documentation to hands-on tutorials, we’re crafting resources to help you succeed with the Spark Operator. We are planning to host regular Spark Operator calls to discuss users issues, questions, and future roadmaps.

  • Open Doors for Contributions: This is a call to arms for developers, writers, and enthusiasts! Your contributions are the lifeblood of this project, and there’s a place for everyone to make a mark.

  • Kubeflow Working Group Data: To consolidate efforts around new data tools in the Kubeflow ecosystem such as Spark Operator and Model Registry the new Working Group Data will be formalized soon. Feel free to review this PR to get involved and provide your feedback on the charter.

Join the Movement

The Kubeflow Spark Operator is more than just software. It’s a community endeavor. Here’s how you can be a part of this journey:

  • Dive In: Visit our GitHub repository to start your journey with the Kubeflow Spark Operator.

  • Contribute: Every code snippet, documentation update, and piece of feedback counts. Find out how you can contribute on GitHub.

  • Be Part of the Community: Join the conversation in the #kubeflow-spark-operator channel on Kubeflow Slack. Whether you’re seeking advice, sharing insights, or just listening in, your presence enriches us. Follow this guide to join Kubeflow Slack and learn more about Kubeflow community.

  • Kubeflow Spark Operator Community Call: We’re excited to announce Spark Operator Community Monthly Meetings for Open Source Contributors starting May 17th, 2024 (10-11 AM PST). These meetings, held every third Friday, are your chance to discuss project updates, share ideas, and collaborate with the community. You can find the Zoom call details and meeting notes in this Google Doc. Please also join the #kubeflow-discuss Google group and find meeting links in the #kubeflow-spark-operator channel.

In the spirit of collaboration fostered on platforms like Slack, and with the generous support of the Google Cloud team, we’re set to sail into a promising future. The Kubeflow Spark Operator isn’t just a tool, it’s our collective step towards harnessing the true potential of Spark on Kubernetes. Together, let’s shape the future of cloud-native big data processing.

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