Streamlining Large-Scale Dataset Migrations with Automated Agents and Fleet Orchestration

Introduction

Migrating thousands of datasets is a daunting challenge that can bring even the most robust engineering teams to a standstill. At Spotify, we faced exactly this problem as our data landscape grew. The traditional manual approach was error-prone, time-consuming, and a major source of operational pain. To solve this, we turned to a powerful combination of Honk (a background coding agent), Backstage (our internal developer portal), and Fleet Management (our infrastructure orchestration layer). This article explains how these three components worked together to supercharge downstream consumer dataset migrations.

Streamlining Large-Scale Dataset Migrations with Automated Agents and Fleet Orchestration — Source: engineering.atspotify.com

The Challenge of Dataset Migrations at Scale

When you have thousands of datasets powering analytics, machine learning models, and product features, any migration becomes a high-stakes operation. Each dataset has its own schema, dependencies, and consumption patterns. Doing this manually meant coordinating across multiple teams, writing custom scripts, and carefully monitoring every step. The risk of breaking downstream consumers was high, and the toll on developer productivity was immense.

Enter the Background Coding Agent: Honk

Honk is our background coding agent — a system that can autonomously execute code-generation tasks, perform transformations, and even write migration scripts. By running in the background, Honk can take a specification (like a new dataset schema) and generate the necessary code to update all downstream consumers. This dramatically reduces the manual effort required and ensures consistency across thousands of datasets.

How Honk Works

Accepts a migration plan defined in a machine-readable format.
Analyzes the current state of all affected datasets.
Generates and applies transformation scripts automatically.
Reports results and flags any anomalies for human review.

The key insight is that Honk does not replace engineers — it amplifies their ability to handle massive scale. Engineers define the rules and boundaries, then Honk executes the grunt work.

Backstage: The Developer Portal That Ties It All Together

Backstage, Spotify’s open-source developer portal, serves as the central hub for all infrastructure and service metadata. For dataset migrations, Backstage provides a unified view of which datasets exist, who owns them, and what services consume them. This context is vital for Honk to know exactly where to apply changes.

Key Integration Points

Service Catalog: Backstage stores the relationships between datasets and their consumers. Honk queries this catalog to scope its work.
Automated Documentation: After a migration, Backstage automatically updates documentation to reflect the new schema, ensuring transparency.
Approval Workflows: Sensitive migrations can be gated using Backstage’s built-in approval steps, adding a safety layer.

Fleet Management: Orchestrating the Migration at Scale

Executing migrations on thousands of datasets in parallel requires careful orchestration. Fleet Management — our system for managing computational clusters — handles the scheduling, resource allocation, and monitoring of Honk agents. It ensures that migration tasks run efficiently without overwhelming the infrastructure.

Fleet Management in Action

Dynamic Scaling: Fleet Management spins up additional compute resources when a large migration batch is queued.
Error Handling: If a migration task fails, Fleet Management retries it with appropriate backoff and alerts the team.
Observability: Real-time dashboards show progress, resource usage, and any bottlenecks.

By combining Honk’s intelligence with Backstage’s context and Fleet Management’s scale, we turned a painful, manual process into a smooth, automated pipeline.

Real-World Impact

Using this integrated approach, we successfully migrated thousands of datasets with minimal human intervention. The time required dropped from weeks to hours. Downstream consumers experienced fewer disruptions because the migrations were consistent and thoroughly tested by Honk. Engineers could focus on high-value tasks instead of repetitive scripting.

Conclusion

Background coding agents like Honk, when paired with a rich developer portal (Backstage) and robust fleet orchestration (Fleet Management), can revolutionize how organizations handle large-scale dataset migrations. The combination reduces risk, saves time, and frees engineers to solve more interesting problems. For teams facing similar challenges, we recommend treating the migration pipeline as a product — invest in automation, context, and scalability from the start.

This article was inspired by Spotify Engineering’s original post on Honk, Part 4.

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