Automating Large-Scale Dataset Migrations with Honk, Backstage, and Fleet Management: A Step-by-Step Guide

Introduction

Migrating thousands of datasets downstream can be a daunting task, often riddled with manual errors, downtime, and developer burnout. At Spotify, we tackled this challenge using a powerful trio: Honk (background coding agents), Backstage (our developer portal), and Fleet Management (orchestration layer). This guide walks you through how to replicate our approach—automating the heavy lifting, providing visibility, and ensuring safe, efficient migrations. By the end, you'll have a blueprint to supercharge your own dataset migrations.

What You Need

• Access to a Honk deployment (background coding agent framework)
• Backstage instance configured with catalog and software templates
• Fleet Management system (e.g., Kubernetes with custom operators or a similar orchestration tool)
• Source and target database endpoints (e.g., Kafka topics, data warehouses, or storage buckets)
• CI/CD pipeline integration (e.g., GitHub Actions, Jenkins)
• Monitoring and alerting system (e.g., Prometheus, Grafana)
• An up-to-date inventory of dataset consumers (using Backstage's catalog)

Step-by-Step Guide

Step 1: Define Migration Tasks as Honk Agents

Start by identifying every dataset and its downstream consumer. Create Honk background coding agents that encapsulate each migration step—read from old source, transform, write to new destination. Write these as isolated, idempotent jobs. Include error handling, retries, and dry-run modes. Register the agents in your Honk control plane.

Step 2: Catalog Datasets and Consumers in Backstage

Use Backstage's software catalog to register each dataset as an entity, along with its owners, usage metadata, and dependencies. This creates a single source of truth. For each consumer (microservice, analytics job, etc.), add an analytics or data-access relation. This enables you to reason about impact before migrations.

Step 3: Design Orchestration with Fleet Management

Model your migration as a workflow in Fleet Management. Define stages: prepare (validate schema), dry-run (test on a subset), cut-over (switch traffic), cleanup (remove old data). Use Fleet’s scheduling to run agents in parallel, respecting resource limits. Integrate with Backstage to fetch entity details and trigger approvals.

Step 4: Build a Self-Service Migration Portal

Leverage Backstage's software templates to offer a self-service UI for data owners. For each dataset, present a “Migrate” button that triggers the Fleet pipeline—with pre-filled parameters from the catalog. This empowers teams to start migrations without deep ops knowledge.

Step 5: Execute Test Migrations

Run a dry-run migration on a small, non-critical dataset. Monitor Honk agents via logs and Fleet dashboards. Check Backstage for consumer status. Verify data integrity with a checksum comparison. If successful, proceed to full-scale.

Step 6: Roll Out in Batches

Group datasets by consumer criticality. Use Fleet’s batch controls to migrate in waves. For each wave, automatically update Backstage entities to reflect the new source endpoint. Notify consumer teams via Backstage’s notification system. Run parallel validations.

Step 7: Monitor, Rollback, and Iterate

Continuously monitor migration metrics (throughput, error rate, latency). In Fleet, define rollback policies: if error rate spikes above threshold, revert to previous state and alert via Backstage. After each batch, collect feedback and improve Honk agents (e.g., optimize query pagination).

Tips for Success

• Start small: Pilot with one dataset and a handful of consumers before scaling to thousands.
• Make agents idempotent: Ensure each Honk agent can be re-run safely—this allows retries without data corruption.
• Use Backstage’s ownership model: Always map a dataset to a team; they can self-approve migrations, reducing bottlenecks.
• Automate rollback: Don’t rely on manual reverts. Program Fleet Management to reverse cut-over if validation fails.
• Visualize the migration in Backstage by adding a custom plugin that shows progress per dataset (source, target, status).
• Document edge cases: For every Honk agent, add comments for non-obvious behavior (e.g., handling tombstone records).
• Celebrate small wins: Each successful wave of dataset migrations reduces technical debt. Share updates via Backstage’s tech insights.