Mastering Dataset Migrations with Background Coding Agents: A Step-by-Step Guide Using Honk, Backstage, and Fleet Management

Introduction

Migrating thousands of datasets across a complex infrastructure can be a daunting task. At Spotify, we faced this exact challenge when we needed to update downstream consumer datasets without causing downtime or data loss. Our solution? A combination of Honk, Backstage, and Fleet Management powered by background coding agents. This guide walks you through a proven strategy to automate and streamline such migrations, reducing manual effort and minimizing risk. Whether you're managing a few dozen datasets or thousands, these steps will help you supercharge your migration process.

Mastering Dataset Migrations with Background Coding Agents: A Step-by-Step Guide Using Honk, Backstage, and Fleet Management — Source: engineering.atspotify.com

What You Need

Honk: A notification and orchestration system for coordinating migration tasks.
Backstage: A developer portal to track and manage services, datasets, and ownership.
Fleet Management: A system to manage and deploy agents across your infrastructure.
Background Coding Agents: Automated scripts or microservices that perform transformation, validation, and migration logic.
Access: Permissions to modify dataset schemas, write agents, and trigger migrations.
Documentation: Current dataset schemas, consumer details, and migration rules.

Step-by-Step Guide

Step 1: Map Your Dataset Landscape

Begin by creating a comprehensive inventory of all datasets that need migration. Use Backstage to register each dataset, including its schema, location, and downstream consumers. This provides a single source of truth and helps identify dependencies. Ensure each dataset has an owner responsible for approving changes. Without this map, you risk missing critical consumers or breaking dependencies.

Step 2: Define Migration Rules and Transformations

For each dataset, specify exactly how the schema or data must change. Create transformation rules as code (e.g., Python scripts) that can be executed by the background agents. For instance, you might need to add a new field, rename columns, or normalize values. Document these rules in Backstage alongside each dataset. This step ensures agents know exactly what to do when a migration is triggered.

Step 3: Build Background Coding Agents

Develop automated agents that execute the migration. Each agent should: read the current dataset, apply transformations, validate the new dataset, and then update it. Use your preferred language (e.g., Python or Go) and integrate with Honk for notifications. Agents should be idempotent—able to run multiple times without causing issues. Package them as containerized services or scripts that can be managed by Fleet Management for easy deployment.

Step 4: Orchestrate with Honk

Set up Honk to trigger agents on a schedule or in response to events (e.g., a dataset version change). Configure Honk to send status updates to Backstage and to notify dataset owners of progress. Honk can also coordinate dependencies: if migration of Dataset A must happen before Dataset B, Honk can enforce that order. Use Honk’s webhooks to integrate with your CI/CD pipeline for seamless launches.

Step 5: Deploy Fleet Management Agents

Use Fleet Management to distribute your background coding agents across your infrastructure. This ensures scalability and resilience—if one agent fails, another can pick up the task. Fleet Management handles load balancing, retries, and monitoring. Configure it to run agents in parallel where possible, but respect resource limits to avoid overloading the system. Monitor logs centrally via Fleet Management’s dashboard.

Step 6: Test Migration with a Subset

Before rolling out to all datasets, select a small, non-critical subset as a pilot. Trigger the migration using Honk and run the background agents. Verify that downstream consumers see the new datasets correctly and that no errors occur. Use Backstage to review any schema changes or data quality issues. Rollback quickly if needed—your agents should support reverting to the previous state. This step builds confidence and catches edge cases early.

Step 7: Roll Out Incrementally

Migrate datasets in waves, starting with low-priority consumers. Use Backstage to group datasets by risk level and schedule migrations accordingly. Honk can send alerts before each wave and after completion. Monitor the Fleet Management dashboard for agent health. If you encounter issues, pause the wave and fix the agents before proceeding. This incremental approach reduces blast radius and allows you to learn from each wave.

Step 8: Validate and Clean Up

After all datasets are migrated, run a final validation pass. Ensure all downstream consumers have updated their references. Use Honk to send a “migration complete” notification. Archive old dataset schemas and remove temporary agents. Backstage should reflect the new state. Finally, write a post-mortem to capture lessons learned and update your documentation.

Tips for Success

Automate rollbacks: Always design agents to revert changes. A failed migration shouldn’t leave data in limbo.
Use dry runs: Run agents in dry mode first to see what would change without applying it.
Communicate early: Notify downstream consumers before each wave via Honk or direct messages. Surprises break trust.
Optimize agent performance: Profile agents to avoid slow transformations. Consider batching updates if datasets are large.
Version your agents: Keep different versions of agents so you can replay migrations exactly as originally executed.
Monitor dependencies: Use Backstage’s dependency graph to ensure you don’t migrate a dataset that is still referenced by others in an older format.

By following these steps and leveraging Honk, Backstage, and Fleet Management, you can turn a painful manual migration into a smooth, automated process. Background coding agents handle the heavy lifting, freeing your team to focus on higher‑value work. This approach has saved Spotify countless hours and eliminated common migration errors. Start small, iterate, and soon you’ll be migrating thousands of datasets with confidence.