Streamlining Dataset Migrations: A Step-by-Step Guide with Honk, Backstage, and Fleet Management

By

Introduction

Migrating thousands of datasets across your infrastructure can be a daunting, error-prone process. Manual efforts often lead to bottlenecks, inconsistencies, and long downtime. At Spotify Engineering, we developed a solution that leverages Honk for background coding agents, Backstage as a developer portal, and Fleet Management for orchestration. This guide walks you through the exact steps to set up automated dataset migrations, reducing pain and supercharging your downstream consumers.

What You Need

• Access to Honk (agent framework for background tasks)
• Backstage instance (developer portal with catalog and plugin support)
• Fleet Management system (for scaling and orchestrating agents)
• Metadata about all datasets (schemas, locations, dependencies)
• A source and target environment for datasets (e.g., S3 buckets or databases)
• Basic familiarity with containerization (Docker) and microservices
• Automation scripts or pipelines (CI/CD tools like Jenkins or GitLab CI)

1. Step 1: Set Up Honk Agents for Migration Tasks

   Honk is your background coding agent framework. Start by creating Honk agents dedicated to dataset migrations. Each agent should encapsulate a specific task: reading source datasets, transforming them, validating schemas, or writing to targets.

   • Define agent types using Honk’s YAML configuration. For example, a dataset-transformer agent that applies mapping rules.
   • Package each agent as a container image using Docker. Include dependencies like Python libraries or JDBC drivers.
   • Register agents in Honk’s registry so they can be discovered and invoked.

   Test agents individually with a small subset of data before scaling.

2. Step 2: Integrate Honk with Backstage for Visibility

   Backstage serves as your single pane of glass for all software components. Use it to monitor and trigger dataset migrations.

   • Create a Backstage plugin that interfaces with Honk’s API. This plugin should list available agents, show their status, and allow manual execution.
   • Add a dataset entity to Backstage’s software catalog. For each dataset, store metadata (schema, size, ownership).
   • Link dataset entities to migration plans. When a dataset needs migration, the planner triggers the appropriate Honk agent.

   This integration enables self-service: teams can request migrations through Backstage without deep knowledge of the underlying infrastructure.

3. Step 3: Configure Fleet Management for Orchestration

   Fleet Management handles scaling and distribution of Honk agents across your cluster. This step ensures that migrating thousands of datasets doesn’t overwhelm resources.

   • Define fleet policies: maximum number of concurrent agents per dataset type, resource limits (CPU, memory), and scheduling rules.
   • Connect Fleet Management to your container orchestrator (e.g., Kubernetes). Agents will run as pods.
   • Set up monitoring dashboards to track fleet health and migration progress.

   Fleet Management also handles retries and failover. If an agent crashes, it restarts on a healthy node.

4. Step 4: Define Migration Workflows

   A migration workflow consists of multiple Honk agents chained together. Define the order of operations for each dataset type.

   • Example workflow: Read source dataset → Transform schema (add/drop columns) → Validate data integrity → Write to target → Verify checksum.
   • Use a workflow definition language (e.g., YAML) to specify which agents to run and how to pass data between them (via shared volumes or message queues).
   • Include conditional branches: if validation fails, trigger a notification back to Backstage and pause the migration.

   Store these workflows as version-controlled artifacts. This makes migrations reproducible and auditable.

5. Step 5: Run Background Coding Agents to Automate Migrations

   Now trigger automated migrations at scale. This is where Honk shines as a “background coding agent” – it runs continuously and processes datasets as they appear.

   

   • Set up a scheduler (e.g., cron job or event-driven trigger) that scans the dataset catalog for pending migrations.
   • For each pending dataset, Honk creates an agent instance and executes the defined workflow. Fleet Management allocates resources dynamically.
   • Monitor progress via Backstage dashboards. Each dataset shows migration status: queued, running, completed, or failed.

   Because agents run in the background, teams can focus on higher-level tasks while migrations proceed automatically.

6. Step 6: Handle Errors and Rollbacks

   Even with automation, errors happen. Build robust error handling into your system.

   • Honk agents should log detailed error messages and send alerts to Backstage. Use Backstage’s notification system for real-time updates.
   • Define rollback procedures: if a migration fails after writing partial data, revert to the previous state (e.g., restore from backup or use idempotent writes).
   • Create a “migration health” entity in Backstage that aggregates failure rates and root causes.

   Regularly review errors to improve agent code and workflows.

7. Step 7: Iterate and Scale

   Once the initial migration pipeline works, refine it for performance and new use cases.

   • Measure throughput: how many datasets can migrate per hour? Adjust Fleet Management resource limits accordingly.
   • Add new dataset types by extending Honk agents and workflow definitions.
   • Consider using Honk’s built-in caching to avoid re-transforming unchanged datasets.
   • Share migration metrics with teams via Backstage to celebrate successes and identify bottlenecks.

   Scalability is key. With this architecture, you can migrate from dozens to thousands of datasets without proportional increase in manual effort.

Tips and Best Practices

• Start small: Pilot the system with 10–20 low-risk datasets before rolling out to all.
• Use idempotent operations: Ensure that running the same migration twice yields the same result. This simplifies retries.
• Version everything: Version your agent code, workflow definitions, and dataset schemas. Use Git to track changes.
• Monitor aggressively: Set up alerts for migration duration, error rates, and resource usage. Use Backstage to centralize logs.
• Involve dataset owners: Let them validate the migrated data via Backstage before marking migration complete.
• Document common failure patterns: Create a wiki or runbook so new team members can debug issues quickly.
• Schedule migrations off-peak: Running during low-traffic hours reduces impact on downstream consumers.

Related Articles

• Why the SECURE Data Act Fails to Protect Consumer Privacy
• Boosting WebAssembly Performance with Speculative Inlining and Deoptimization
• React Native 0.85: Performance Upgrades and Developer Experience Enhancements
• How to Snag the Best Electric Ride Deals: A Step-by-Step Savings Guide
• Breaking: Yozma IN 10 Electric Dirt Bike Hits Record $999 Low Amid Major EcoFlow and Anker Power Station Sales
• Electrifying Fleets and Solar Incentives: A Race Against Time and Oil
• What You Need to Know About Tesla's 1 Million Humanoid Robots
• How to Evaluate the SECURE Data Act and Its Impact on Your Privacy