Automating Dataset Migrations with Background Coding Agents: A Practical Guide

Overview

Migrating thousands of downstream consumer datasets is a daunting task—each dataset may have unique schemas, dependencies, and transformation logic. At Spotify, we tackled this challenge by combining three internal tools: Honk (an agent-based workflow engine), Backstage (a developer portal for service cataloging), and Fleet Management (for orchestrating distributed workers). This guide walks you through how to set up a similar system to automate dataset migrations, reduce manual effort, and avoid common pitfalls. By the end, you'll have a blueprint for deploying background coding agents that handle the heavy lifting of schema changes, data transfer, and downstream compatibility checks.

Prerequisites

• Agent orchestration platform (e.g., Honk, Apache Airflow, or Kubernetes-native agents)
• Service catalog tool (e.g., Backstage with custom plugins)
• Fleet management system (e.g., Nomad, Kubernetes, or a custom worker pool)
• Dataset metadata store (e.g., a database tracking schema versions, owner info, and downstream consumers)
• Basic knowledge of YAML, Python (for custom agents), and REST APIs

Step-by-Step Instructions

1. Setting Up Honk for Dataset Discovery

Honk agents are lightweight containers that execute predefined tasks. First, define an agent that scans your metadata store for datasets pending migration:

# agent_discovery.yaml
name: dataset-scanner
image: honk-agent:latest
command: python scanner.py
schedule: "0 */6 * * *"  # every 6 hours
env:
  - METADATA_API: https://metadata.internal
  - OUTPUT_TOPIC: honk.actions.migrate
volumes:
  - /tmp/scan-results:/data

The scanner generates a list of datasets (IDs, current version, target version) and publishes them to a message queue. Honk picks up these messages to trigger migration workflows.

2. Configuring Backstage Integration

Backstage acts as the single pane of glass for dataset ownership and migration status. Create a custom plugin that visualizes the migration pipeline:

// migration-plugin.ts
import { createPlugin, createRoutableExtension } from '@backstage/core-plugin-api';
export const migrationPlugin = createPlugin({
  id: 'dataset-migration',
  routes: {
    root: '/dataset-migration/createRoutableExtension',
  },
});
export const MigrationPage = migrationPlugin.provide(
  createRoutableExtension({
    name: 'MigrationPage',
    component: () => import('./components/MigrationPage').then(m => m.MigrationPage),
    mountPoint: migrationPlugin.routes.root,
  }),
);

Register the plugin in your Backstage app and expose endpoints for Honk agents to report progress. Use Backstage's entity relation API to link datasets to their downstream consumers.

3. Deploying Fleet Management Workers

Fleet Management (e.g., a Nomad cluster) runs the actual migration agents. Define a job for each dataset migration step:

# migrate-dataset.nomad
job "migrate-dataset" {
  datacenters = ["dc1"]
  group "workers" {
    count = 1  # number of parallel migrations
    task "transform" {
      driver = "docker"
      config {
        image = "migration-agent:1.0"
        args = ["--dataset-id", "${NOMAD_META_DATASET_ID}", "--target-version", "v3"]
      }
      resources {
        cpu    = 500
        memory = 1024
      }
    }
  }
}

The agent performs schema transformation, data copy, and validation. After completion, it updates the metadata store and notifies Backstage.

4. Executing the Migration Pipeline

Chain the components together with a workflow definition. In Honk, a simple DAG might look like:

workflow:
  name: dataset-migration
  steps:
    - name: discover
      agent: dataset-scanner
    - name: validate-dependencies
      agent: dependency-checker
      depends_on: discover
    - name: execute-migration
      agent: fleet-manager
      depends_on: validate-dependencies
    - name: notify-consumers
      agent: email-sender
      depends_on: execute-migration

Monitor progress via Backstage dashboards. Each agent logs its status to a central topic, and Fleet Management handles retries on failure.

Common Mistakes and How to Avoid Them

• Ignoring downstream compatibility: Always validate that new dataset schemas don't break existing queries. Use a compatibility checker agent that runs before migration.
• Insufficient error handling: Agent code should be idempotent—if a migration fails mid-way, the retry should pick up where it left off (e.g., using checkpoint files).
• Overloading Fleet Management: Limit concurrent migrations to the number of free worker nodes. Use resource quotas (CPU/memory) to avoid cluster saturation.
• Not updating Backstage metadata: After migration, the dataset's entity in Backstage must reflect the new version. Otherwise, downstream teams get stale information.

Summary

Automating dataset migrations with background coding agents—Honk for workflow orchestration, Backstage for visibility, and Fleet Management for execution—dramatically reduces manual effort and risk. By following the steps above, you can build a resilient pipeline that discovers datasets, performs schema transformations, and notifies stakeholders, all while avoiding common pitfalls like compatibility gaps and resource exhaustion. Start small: migrate a handful of low-criticality datasets, then scale up.