From weeks to minutes: The new agentic era of data pipelines

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Data pipelines are the backbone of the modern enterprise, yet a barrier to entry exists for orchestrating them, making this critical capability unavailable to many data professionals. Following our announcements at Google Cloud NEXT ’26, where we introduced the Orchestration Pipelines framework, we are fundamentally changing this dynamic. To bring this powerful framework directly to practitioners, we offer the Data Agent Kit — a unified, freely available, and open-source collection of data engineering and data science tools that integrate directly into your preferred IDE or CLI (such as VS Code, Claude Code, or Codex). The Data Agent Kit seamlessly embeds the Orchestration Pipelines framework into your workflow in two distinct ways. First, it provides a dedicated Data Engineering tab for comprehensive pipeline management. Second, it includes a specialized agentic skill designed to author, deploy, and troubleshoot production-grade Apache Airflow® DAGs using natural language. By pairing these specialized agent skills with a declarative YAML DSL, all data personas — from analysts to ML engineers — can bypass complex Python Airflow boilerplate. This framework decouples high-level orchestration logic from underlying compute execution, democratizing access to powerful MLOps capabilities across your entire data organization. In this post, we will walk through an exemplary MLOps use case to demonstrate how easily this can be achieved. Setting up your environment Before authoring your first Orchestration Pipeline, you need to set up your local development environment. Getting …

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• Originalquelle / Hersteller-Advisory lesen: https://cloud.google.com/blog/products/data-analytics/build-data-pipelines-in-less-time-with-data-agent-kit/
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