Automating Dataset Migrations with Background Coding Agents: A Practical Guide

Overview

Migrating thousands of downstream consumer datasets is a daunting task. At Spotify, we faced exactly this challenge—and overcame it by combining three powerful tools: Honk (our background coding agent framework), Backstage (the developer portal), and Fleet Management (our service orchestration system). This tutorial walks you through how to design and execute a similar large-scale migration using background coding agents to supercharge the process, reduce manual effort, and minimize downtime.

By the end of this guide, you’ll understand how to set up Honk agents that automatically detect, transform, and validate dataset migrations across hundreds of services—all coordinated through Backstage and scaled with Fleet Management.

Prerequisites

Before diving in, ensure you have the following ready:

• Backstage instance (or similar developer portal) with service catalog populated
• Honk framework deployed in your infrastructure
• Fleet Management system capable of running containers or jobs at scale
• Basic understanding of dataset schemas, migrations, and event-driven architectures
• Access to downstream service repositories (e.g., GitHub, GitLab) with CI/CD pipelines

If you’re new to any of these tools, consider reviewing their official documentation first. This guide assumes you have a functional setup.

Step-by-Step Instructions

1. Define the Migration Scope and Pattern

Start by analyzing the datasets you need to migrate. In our case, we had thousands of downstream consumers relying on a legacy schema. Identify common patterns: field renames, type changes, or structural shifts. Create a migration specification document (YAML or JSON) that describes the transformation rules. This will be the input for your Honk agents.

Example migration rule:

migration:
  source_schema: "v1"
  target_schema: "v2"
  transforms:
    - field: "user_id"
      rename: "account_id"
    - field: "timestamp"
      type: "string" -> "datetime"

2. Build Honk Background Coding Agents

Honk agents are small, autonomous programs that run in the background and perform a single task: reading the migration specification, fetching the current dataset schema from each service, applying the transformation, and writing the new version. Each agent is responsible for a subset of services.

Write your Honk agent in Python (or your language of choice) using the Honk SDK:

from honk import Agent, MigrationTask

class SchemaMigrator(Agent):
    def process(self, task: MigrationTask):
        service = task.service
        current_schema = self.fetch_schema(service)
        new_schema = self.transform(current_schema, task.rules)
        self.deploy_schema(service, new_schema)
        self.notify_backstage(service, status="migrated")

Register the agent with Fleet Management so you can scale it across multiple workers.

3. Coordinate via Backstage

Backstage becomes the central hub for tracking migration progress. Create a new Backstage plugin (or use an existing one) that lists all downstream services and their migration status. For each service, the plugin should trigger a Honk migration job when approved. Use Backstage’s Scaffolder to generate migration tickets automatically.

Connect Backstage to your Fleet Management API so that approving a migration in Backstage launches a fleet of Honk agents.

4. Implement the Migration Pipeline

The full pipeline works as follows:

1. Discovery: Honk agents scan the service catalog in Backstage to identify which services still use the old schema.
2. Staging: The agent clones the service repository, applies the transformation in a branch, and runs validation tests (e.g., check that downstream dashboards still work).
3. Approval: A pull request is created in Backstage, assigned to the service owner for review.
4. Execution: Once approved, the agent merges the PR and triggers a deployment via Fleet Management.
5. Verification: The agent monitors the deployment and reports back to Backstage the migration status (success or rollback).

This loop repeats until all services are migrated.

5. Scale with Fleet Management

Fleet Management allows you to run hundreds of Honk agents in parallel. Configure job templates that define CPU, memory, and timeout. Use a queue system (e.g., RabbitMQ or AWS SQS) to distribute migration tasks across agents. Monitor agent health and restart failed ones automatically.

fleet_template:
  name: "honk-migration-worker"
  image: "honk-agent:latest"
  replicas: 50
  resources:
    cpu: "1"
    memory: "2Gi"
  queue: "migration-tasks"

6. Handle Edge Cases and Retries

Not all migrations go smoothly. Implement idempotency in your Honk agents so they can safely retry. If a migration fails (e.g., schema incompatibility), the agent should log the error, revert changes, and flag the service as blocked in Backstage. Then a human can investigate.

Common Mistakes

• Not testing transformations thoroughly: Small schema changes can break downstream consumers. Always run validation tests in a staging environment first.
• Ignoring service dependencies: Some services depend on others. Migrate in dependency order to avoid downstream cascading failures.
• Overloading Fleet Management: Spinning up too many agents at once can overwhelm your infrastructure. Start with a small batch and ramp up.
• No rollback plan: Even with automated agents, things go wrong. Ensure every Honk agent has a rollback mechanism (e.g., revert PR, restore old schema).
• Forgetting to notify teams: Backstage is great, but direct communication (Slack, email) when a migration is pending or completed reduces surprises.

Summary

By combining Honk background agents, Backstage orchestration, and Fleet Management scaling, we turned a painful manual migration of thousands of datasets into an automated, reliable process. The key is to define clear transformation rules, build self-contained agents, and leverage Backstage for visibility and approval. This approach minimizes human error, speeds up migrations, and keeps downstream consumers happy. You can adapt this pattern to any large-scale data migration challenge in your own organization.