Streamlining Massive Dataset Migrations with Background Coding Agents

Introduction

Migrating thousands of datasets across a complex ecosystem is a daunting task. At Spotify, we faced exactly this challenge when moving downstream consumer datasets to new infrastructure. To ease the pain, we built a system combining Honk, Backstage, and Fleet Management—leveraging background coding agents to automate and supercharge the process. This article explores how these components work together to make large-scale migrations manageable, reliable, and developer-friendly.

The Challenge of Large-Scale Dataset Migrations

When you have thousands of datasets consumed by various downstream services, even a simple schema change can ripple into a massive coordination effort. Manual migration is error-prone, slow, and often blocked by dependencies. Engineers need to identify affected consumers, update configurations, test changes, and roll out in a controlled manner. Without automation, the process can take weeks or months, delaying critical improvements and increasing risk.

Common Pain Points

• Discovery: Finding all consumers of a dataset is non-trivial in a distributed architecture.
• Coordination: Different teams own different consumers, requiring cross-team communication.
• Consistency: Ensuring all consumers are updated correctly and in sync.
• Rollback: If something goes wrong, rolling back a change across hundreds of services is a nightmare.

Introducing Honk and Background Coding Agents

Honk is Spotify’s internal platform for background coding agents—autonomous programs that can read, modify, and test code across repositories. These agents operate asynchronously, triggered by events like a dataset schema change. They automatically update downstream consumer code, create pull requests, and run validation checks. This removes the manual burden from developers and speeds up migrations significantly.

Key features of Honk agents:

1. Automated code modifications: Agents can transform consumer code to match new dataset formats.
2. Multi-repository support: They work across thousands of repos, handling diverse consumer implementations.
3. Safety checks: Each change is tested in isolation before being proposed.

Leveraging Backstage for Developer Experience

Backstage, Spotify’s open-source developer portal, provides a unified interface for managing services, datasets, and migrations. In our workflow, Backstage serves as the control plane:

• Service Catalog: All downstream consumers are registered, making discovery straightforward.
• Migration dashboards: Engineers see real-time status of each dataset migration—how many consumers updated, which are pending, and any failures.
• Trigger actions: Developers can launch Honk agents directly from Backstage with a single click.

By integrating Honk with Backstage, we provide a seamless experience: engineers no longer need to dig through multiple tools. They can monitor progress, review changes, and approve merges from one central location.

Fleet Management for Orchestration

To run thousands of migration tasks reliably, we use Fleet Management—an orchestration layer that schedules and executes Honk agents across our infrastructure. Fleet handles:

• Resource allocation: Spinning up agent instances based on demand.
• Rate limiting: Preventing overload on repositories and CI systems.
• Error handling and retries: Automatically retrying failed agents with exponential backoff.
• Monitoring: Collecting metrics on agent success rates and durations.

Fleet Management ensures that even during peak migration periods, the system remains stable and responsive. It also allows us to parallelize tasks safely, reducing overall migration time from weeks to hours.

How the Pieces Fit Together

The workflow for a typical dataset migration looks like this:

1. Trigger: A dataset owner publishes a schema change in the registry. Backstage captures the event and creates a migration ticket.
2. Discovery: Backstage’s Service Catalog identifies all downstream consumers that depend on the dataset.
3. Agent deployment: Fleet Management launches a Honk agent for each consumer repository.
4. Code update: The agent reads the current consumer code, applies the necessary changes (e.g., updating field names, types, or serialization), and runs unit tests.
5. Pull request creation: If tests pass, the agent creates a pull request in the consumer’s repository with a detailed description of the change.
6. Review and merge: The consumer team reviews the PR (often automated through pre-approved policies) and merges it.
7. Tracking: Backstage updates the migration dashboard, showing which consumers are done and which remain.

This fully automated pipeline drastically reduces human intervention. Developers only need to step in for exceptional cases—like when a consumer requires custom logic that the agent cannot infer.

Benefits and Outcomes

Since adopting this system, we have migrated thousands of datasets with high success rates. Key outcomes include:

• Speed: Migration time dropped from weeks to days (or even hours for simple changes).
• Reliability: Automated testing catches regressions early, reducing production incidents.
• Developer satisfaction: Engineers spend less time on tedious updates and more on feature work.
• Scalability: The system handles an ever-growing number of consumers without proportional increases in human effort.

Conclusion

Background coding agents, powered by Honk, Backstage, and Fleet Management, have transformed how we handle downstream consumer dataset migrations at Spotify. By automating the most painful parts of the process, we’ve made migrations faster, safer, and less stressful. If you’re facing similar challenges at scale, consider building a similar pipeline—your developers will thank you.

Originally published on Spotify Engineering.