Automating Large-Scale Dataset Migrations with Background Coding Agents

Overview

Migrating thousands of datasets across a large organization is a daunting task. Manual interventions are error-prone, slow, and costly. Spotify Engineering tackled this challenge by combining three powerful tools: Honk (a background job system), Backstage (developer portal for service discovery and infrastructure), and Fleet Management (for orchestrating compute resources). This tutorial provides a step‑by‑step guide to building a similar system—background coding agents that automate downstream consumer dataset migrations, reducing pain and increasing reliability.

Prerequisites

Before diving into the implementation, ensure you have:

• Basic knowledge of dataset schemas and migration strategies (e.g., column renames, type changes, partition evolution).
• Access to an Honk cluster or a comparable background job platform. Honk is a scalable, fault‑tolerant system for running asynchronous tasks.
• A Backstage instance (version 1.0+) configured with service catalog and tech docs plugins. Backstage will serve as the control plane for triggering and monitoring migrations.
• Fleet Management infrastructure (e.g., Kubernetes, Nomad, or AWS ECS) capable of dynamically provisioning containers for agent tasks.
• Python 3.8+ and a data processing framework like Apache Spark or Pandas for writing migration scripts.

Step‑by‑Step Instructions

1. Define the Migration Workflow in Backstage

Backstage provides a unified interface for developers to manage services. Start by creating a custom Backstage plugin that exposes a migration trigger endpoint.

1. Scaffold a new Backstage plugin using the CLI: backstage-cli create --plugin dataset-migration.
2. In the plugin, define a schema for migration requests: dataset name, source version, target version, and scheduling options.
3. Create a frontend component (React) that reads from your service catalog and allows users to initiate a migration with a few clicks.
4. When a migration is requested, the plugin calls a backend API that enqueues a job in Honk.

2. Implement the Honk Job Executor

Honk runs background tasks. Each migration will be a separate job. Create a Honk job class in Python:

from honk import HonkJob

class DatasetMigrationJob(HonkJob):
    def handle(self, dataset_id, source_schema, target_schema):
        # 1. Read current dataset
        # 2. Transform to target schema
        # 3. Write to new location (or in‑place)
        # 4. Update catalog metadata
        pass

Ensure the job handles errors gracefully and retries on transient failures. Honk automatically provides retry logic, but you can override with custom backoff.

3. Configure Fleet Management for Agent Scaling

Fleet Management dynamically provisions agents (containers) to execute Honk jobs. Create a Fleet configuration that defines:

• Base image: a container with your migration scripts, Spark/Pandas, and the Honk client.
• Resource limits: CPU, memory, and GPU if needed for large datasets.
• Auto‑scaling rules: e.g., scale up to 20 agents when queue depth exceeds 50.
• Health checks: heartbeat signals to detect stuck agents.

Deploy the configuration using your Fleet Management CLI or API.

4. Wire Everything Together

Connect Honk, Backstage, and Fleet Management:

1. In Backstage, when a migration request is submitted, call Honk’s API to create a new job with parameters from the request.
2. Honk’s scheduler picks up the job and sends it to an available agent managed by Fleet.
3. The agent runs the DatasetMigrationJob.handle() method, performing the transformation.
4. Upon completion, the agent updates Backstage’s entity metadata (e.g., schema version) via Backstage’s catalog API.
5. Optionally, send a notification (Slack, email) to the requesting developer.

5. Monitor and Observe

Implement dashboards for migration status. Use Honk’s built‑in metrics (job count, failure rates) and push them to Prometheus. In Backstage, create a “Migration History” view that shows:

• Dataset name
• Current schema version
• Migration start/end time
• Success/failure status
• Logs from the agent (fetched from Fleet’s logging backend)

Common Mistakes

Neglecting Schema Compatibility

Applying a migration without considering downstream consumers can break dashboards or ETL pipelines. Always verify that the target schema is backward‑compatible or communicate breaking changes via Backstage’s catalog annotations.

Underprovisioning Agents

Thousands of datasets require efficient parallel execution. If Fleet Management has too few agents, queue times skyrocket. Monitor queue depth and set aggressive auto‑scaling thresholds.

Ignoring Idempotency

Network issues or agent restarts can cause duplicate job executions. Ensure your migration scripts are idempotent: running them multiple times should produce the same final state. Use transactional writes or checksum validation.

Poor Error Handling in Honk Jobs

A single unhandled exception can kill the agent and waste resources. Wrap your migration logic in try/except blocks and return meaningful error messages to Honk and Backstage.

Summary

This tutorial demonstrated how to supercharge downstream consumer dataset migrations by combining Honk, Backstage, and Fleet Management. You defined a migration workflow in Backstage, implemented a Honk job executor, configured Fleet for agent scaling, and integrated monitoring. Avoiding common pitfalls like schema incompatibility and insufficient scaling ensures reliable, automated migrations for thousands of datasets.

By adopting this approach, your team can reduce manual overhead, accelerate schema evolution, and maintain high data quality across the organization.