How to Automate Dataset Migrations with Background Coding Agents Using Honk, Backstage, and Fleet Management

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Introduction

Migrating thousands of datasets across a large engineering organization is a daunting task. It often involves manual scripts, coordination headaches, and high risk of errors. At Spotify, we tackled this challenge by combining three powerful tools: Honk (our background coding agent framework), Backstage (our developer portal), and Fleet Management (our deployment orchestration layer). This guide shows you how to replicate our approach to supercharge downstream consumer dataset migrations, turning a painful process into an automated, scalable workflow.

What You Need

• Honk installed and configured in your infrastructure (or an equivalent agent-based coding system).
• Backstage deployed and populated with dataset and service metadata (e.g., entity catalogs, ownership info).
• Fleet Management tooling (e.g., a deployment orchestrator that can run jobs across microservices).
• Access to source control (e.g., GitHub, GitLab) for code generation and versioning.
• Basic familiarity with Python or your agent scripting language.
• Permissions to trigger migrations on downstream consumer services.

Step-by-Step Guide

Step 1: Define Migration Rules and Templates in Honk

Start by creating a set of migration rules that describe how a dataset should be transformed. In Honk, this means writing a template script that encodes the migration logic (e.g., converting a JSON field to a new schema, renaming columns, or changing data types). Use Honk’s declarative DSL to specify:

• Source dataset schema (e.g., old_schema).
• Target dataset schema (e.g., new_schema).
• Transformation functions (e.g., rename_field, cast_to_type).
• Rollback logic for safety.

Store these templates in a version-controlled repository so they can be reviewed and reused.

Step 2: Catalog Datasets and Downstream Consumers in Backstage

Backstage serves as the single source of truth for all services and datasets. Use its entity catalog to register each dataset and its downstream consumers (services that read from the dataset). Add metadata like:

• Dataset owner and maintainer.
• Current schema version.
• Dependencies and impact analysis.
• Migration status.

This step is crucial because Honk and Fleet Management will query Backstage to discover which datasets need migration and which services are affected. Set up automated data lineage tracking so Backstage stays up-to-date.

Step 3: Deploy Background Coding Agents to Analyze and Generate Scripts

Now it’s time to put Honk agents to work. Deploy them as background jobs that periodically scan Backstage catalog for datasets flagged for migration. For each dataset, the agent:

• Fetches the current schema and consumer list from Backstage.
• Selects the appropriate Honk template from Step 1.
• Generates a migration script tailored to that dataset and its downstream services.
• Pushes the script to a dedicated branch in the consumer’s repository (e.g., migration//).

Use Honk’s built-in configuration to control agent concurrency, retries, and error handling. The agents can run as Kubernetes jobs or serverless functions.

Step 4: Orchestrate Deployment with Fleet Management

Fleet Management picks up the generated migration scripts and coordinates their rollout across the fleet. Configure a migration workflow that:

• Validates the script (syntax check, dry run on a subset of data).
• Creates a deployment plan with canary and batch steps.
• Deploys to a small percentage of consumers first, monitors for errors, then rolls out to all.
• Runs rollback automatically if failure thresholds are exceeded.

Integrate Fleet Management with Backstage so that each migration’s status (pending, running, completed, failed) is visible in the developer portal. This gives teams transparency without needing to chase logs.

Step 5: Monitor, Verify, and Iterate

After migration, trigger a validation agent (another Honk job) to compare source and target datasets. Check for:

• Data integrity (row counts, checksums).
• Schema compliance (all required fields present).
• Performance impact (latency increase).

If validation fails, Fleet Management can automatically roll back the affected consumer. Use Backstage dashboards to track overall migration progress. Collect feedback from downstream teams and update Honk templates to handle edge cases. Over time, this process becomes a self-service pipeline that minimizes manual toil.

Tips for Success

• Start small: Pilot with a single dataset and a few consumers before scaling to thousands. This helps you tune Honk templates and Fleet Management workflows.
• Use dry runs: Have Honk agents generate migration scripts but not execute them—let teams review the code first. Once trust is built, enable automated execution.
• Leverage Backstage’s ownership field: Notify dataset owners automatically when a migration is about to affect their service. This prevents surprises.
• Design for rollback: Every Honk template should include a revert script. Fleet Management can then switch deployments quickly if something goes wrong.
• Monitor agent health: Background coding agents can fail silently. Set up alerts for agent failures, stuck jobs, or long runtimes.
• Document the process: Even with automation, clear documentation helps new team members understand the migration pipeline. Embed it in Backstage tech docs.

By combining Honk’s code generation, Backstage’s catalog, and Fleet Management’s deployment coordination, you can turn dataset migrations from a weeks-long pain point into a smooth, automated process. The key is letting the tools do the heavy lifting while you focus on exception handling and continuous improvement.