Automating Large-Scale Dataset Migrations with Background Coding Agents: A Practical Guide

Overview

Migrating thousands of datasets is a daunting task—especially when downstream consumers rely on them. At Spotify, we built a system that uses background coding agents (powered by Honk), combined with Backstage and Fleet Management, to supercharge these migrations. This guide walks you through our approach, from planning to execution, so you can apply similar patterns in your own infrastructure.

Automating Large-Scale Dataset Migrations with Background Coding Agents: A Practical Guide — Source: engineering.atspotify.com

The core idea is simple: instead of manually migrating datasets one by one, you deploy automated agents that perform the heavy lifting in the background. These agents are orchestrated via Backstage templates and managed at scale by Fleet Management. The result? Faster, safer, and less painful migrations.

Prerequisites

Before diving in, ensure you have:

Familiarity with Backstage – You should know how to create custom templates and software catalogs.
Honk agent setup – Honk is our background coding agent framework; you'll need a cluster or deployment where agents can run.
Fleet Management access – This orchestrates agent tasks across multiple nodes. Admin privileges are required to define fleets and schedules.
Dataset schema knowledge – Understand the structure of the datasets you're migrating (e.g., Hive tables, Parquet files).
Testing environment – A non-production environment to validate migrations before rolling out to production.

Step-by-Step Migration Guide

1. Define Migration Tasks in Backstage

Backstage serves as your developer portal and template engine. Create a custom template for dataset migration tasks. Each template should include fields like source dataset path, target schema, migration script URL, and consumer impact flag.

Example Backstage template YAML:

apiVersion: backstage.io/v1alpha1
kind: Template
metadata:
  name: dataset-migration
spec:
  parameters:
    - title: Migration Details
      properties:
        sourcePath:
          type: string
          description: HDFS path of source dataset
        targetPath:
          type: string
          description: HDFS path after migration
        scriptUrl:
          type: string
          description: URL to migration Python script
        consumers:
          type: array
          items:
            type: string
          description: List of downstream consumer teams
      required:
        - sourcePath
        - targetPath
        - scriptUrl
  steps:
    - id: run-migration
      name: Run Honk Agent
      action: honk:run-agent
      input:
        sourcePath: ${{ parameters.sourcePath }}
        targetPath: ${{ parameters.targetPath }}
        scriptUrl: ${{ parameters.scriptUrl }}

Once the template is saved, any team can request a migration via Backstage's UI, triggering an automatic Honk agent job.

2. Set Up Honk Agents

Honk is a lightweight agent that executes code in isolated environments. You'll deploy a fleet of agents (e.g., on Kubernetes) that listen for migration tasks.

Agent configuration example (JSON):

{
  "agentId": "migration-agent-1",
  "maxTasks": 5,
  "workingDir": "/tmp/honk",
  "scriptsBucket": "s3://migration-scripts",
  "timeoutSeconds": 3600
}

Each agent pulls the migration script from a central bucket, executes it against the dataset, and reports status back to Fleet Management.

3. Orchestrate with Fleet Management

Fleet Management is responsible for queuing tasks, assigning them to available agents, and handling retries. Define a fleet that pools multiple Honk agents:

fleet create --name migration-fleet --min-agents 10 --max-agents 50 --image honk:2.1.0

Then schedule a migration batch:

fleet submit --fleet migration-fleet --task-definition migration-task --count 2000

This launches 2000 migration tasks across the fleet, each handled by an agent. Fleet Management ensures no single agent is overwhelmed and retries failed tasks automatically.

4. Execute and Monitor

Once agents start working, you can monitor progress via Fleet Management's dashboard or Backstage's service health pages. Look for these metrics:

Task completion rate – Are agents finishing migrations on time?
Error rate – How many tasks fail? Common failures include permission issues or schema mismatches.
Consumer impact – Are downstream services encountering errors during the migration window?

If you notice a spike in failures, you can pause the fleet, inspect agent logs, and redeploy fixes without affecting completed migrations.

5. Validate Downstream Consumers

The final step—and often the most critical—is validating that consumers of the migrated datasets continue to work correctly. Use Backstage to notify affected teams (based on the consumers field in your template). Provide a validation testing script that compares queries against old and new datasets.

Example consumer validation test:

# validate_migration.py
import pandas as pd

old = pd.read_parquet("hdfs://old-dataset/")
new = pd.read_parquet("hdfs://new-dataset/")

assert old.shape == new.shape, "Row count mismatch!"
assert old.equals(new), "Data mismatch!"
print("Validation passed.")

Automate this check as part of your CI/CD pipeline after each batch migration.

Common Mistakes

Ignoring Consumer Impact

One of the biggest pitfalls is migrating datasets without coordinating with downstream teams. A sudden schema change can break dashboards, reports, or live services. Always use the consumers field in Backstage to notify stakeholders and schedule migrations during low-traffic windows.

Insufficient Testing

Don't skip testing on a subset of datasets. Migrate a small representative sample first, verify end-to-end, then scale up. Many teams rush to migrate thousands of datasets without a dry run, leading to widespread failures.

Forgetting Rollback Plans

Every migration should have a rollback strategy. Snapshot the original dataset before starting. If something goes wrong, you can restore quickly. Fleet Management can be configured to reverse a batch if error thresholds are exceeded.

Misconfiguring Honk Agents

Agents need proper resource limits (CPU, memory) and access permissions. If an agent can't read the source dataset or write to the target, the task hangs forever. Double-check network policies and IAM roles.

Summary

By combining Backstage for task definition, Honk agents for background execution, and Fleet Management for orchestration, you can migrate thousands of datasets with minimal manual effort and reduced risk. The key is to treat migrations as automated, observable, and fault-tolerant processes. Start with a pilot, iterate on your templates, and always keep downstream consumers in the loop. With this pattern, dataset migrations become a routine—almost boring—operation.