Automating Large-Scale Dataset Migrations with Background Coding Agents

Introduction

Migrating thousands of datasets across a complex microservice architecture can be a daunting task, often fraught with manual errors, downtime risks, and coordination nightmares. At Spotify, we faced exactly this challenge—and we solved it by combining three powerful tools: Honk (our background coding agent framework), Backstage (our developer portal), and Fleet Management (our service orchestration layer). This guide breaks down the exact step-by-step process we used to turn a painful migration into a smooth, automated workflow. By the end, you’ll have a blueprint to apply similar principles to your own dataset migrations.

What You Need

• Honk – A background job execution system capable of running code agents asynchronously (or an equivalent task runner like Celery, Airflow, or a custom Kubernetes job scheduler).
• Backstage – A developer portal with a service catalog (open source or internal) to track all your microservices and their dependencies.
• Fleet Management – A tool to manage service deployments, rolling updates, and health checks (e.g., Kubernetes Deployments, Spinnaker, or a custom orchestration engine).
• Database Migration Scripts – Pre-written SQL or NoSQL transformation queries, index changes, or schema updates for each target dataset.
• CI/CD Pipeline – Automated testing and deployment infrastructure (e.g., Jenkins, GitHub Actions, GitLab CI).
• Monitoring Stack – Metrics, logging, and alerting (e.g., Prometheus, Grafana, ELK) to track migration progress and failures.
• Access Control Permissions – Service accounts or tokens with read/write access to source and target databases.

Step 1: Catalog All Downstream Consumers in Backstage

Before you can migrate anything, you need a complete inventory of every service that consumes the datasets you intend to move. In Backstage, create or update Component entities for each microservice, including metadata about which databases and tables they read from or write to.

• Use Backstage’s catalog ingestion to automatically discover services from your infrastructure (e.g., Kubernetes namespaces, Terraform state).
• Add custom annotations like database.source and database.target to each component.
• Run a script to validate that every dataset referenced in code is tracked in Backstage. This becomes your single source of truth.

Step 2: Define Migration Specifications per Dataset

For each dataset, write a migration specification in a machine-readable format (YAML or JSON). This spec should include:

• Source connection string (DB host, port, credentials from a vault).
• Target connection string (new cluster or schema).
• Transformation rules (e.g., column renaming, data type casts).
• Validation queries to run before, during, and after the migration.
• Rollback instructions in case of failure.

Store these specs in a dedicated repository or alongside the dataset’s codebase. Backstage can link to them via its TechDocs feature.

Step 3: Implement Honk Background Coding Agents

Now comes the core automation. Honk agents are small, idempotent programs that execute the migration steps defined in Step 2. Each agent runs in an isolated environment (container or VM) and communicates with Honk’s task queue.

1. Create an agent template – Write a Python or Go script that reads a migration spec, connects to source and target databases, and performs the data transfer. Use batch processing to handle large volumes.
2. Register the agent in Honk – Honk discovers agents via a registry (e.g., a config file or Backstage catalog). Assign a unique name like dataset-migrator-agent.
3. Implement idempotency – Each agent should check a migration_state table before starting. If a migration for that dataset is already in progress or complete, skip or resume.
4. Add progress callbacks – Honk agents emit heartbeat signals and percentage completion metrics to a shared Prometheus endpoint.

Step 4: Orchestrate with Fleet Management

Migrating thousands of datasets in parallel would overwhelm databases. Use Fleet Management to control the rollout:

• Group datasets into batches (e.g., by criticality, size, or owning team). Assign each batch a canary status.
• Define a migration pipeline in Fleet Management: for each batch, trigger a Honk job, wait for completion, run validation checks, and then increment a rollout percentage.
• Use gradual rollout – start with 1% of datasets, then 5%, 10%, etc. For each step, alert if error rates spike.
• Integrate with Backstage’s Scorecards to track which services have completed migration.

Step 5: Automate Pre- and Post-Migration Health Checks

Before migration, Honk agents run pre-flight checks (e.g., source DB connectivity, availability of target free space, compatibility of schema). After migration, they run validation queries comparing row counts, checksums, or sample data.

• If checks fail, the agent automatically reverts the migration and logs the issue to Backstage’s issues tracker.
• For success, the agent updates Backstage component annotations to reflect the new dataset location.

Step 6: Monitor and Iterate

Your migration is never truly “done” until all downstream services have been updated to point to the new dataset locations. Use Fleet Management to trigger service config updates (e.g., updating environment variables in Kubernetes ConfigMaps).

• Set up dashboards in Grafana showing migration throughput, error rate, and remaining dataset count.
• Create runbooks in Backstage TechDocs for common failures (e.g., “Connection timeout – increase retry delay”).
• After all datasets are migrated, run a final consistency check across the entire fleet to ensure no stale references remain.

Tips for Success

• Start with non-critical datasets. Practice the pipeline on test or staging environments before touching production.
• Invest in idempotency. If an agent crashes mid-migration, it should pick up where it left off without duplicating data.
• Use Backstage as the central hub. All status updates, approval gates, and documentation should live there. It becomes your single pane of glass.
• Limit parallel executions. Even with fleet management, too many concurrent Honk agents can throttle your databases. Set a concurrency cap (e.g., 10 at a time).
• Communicate proactively. Use Slack or Mail integrations to notify service owners when their dataset is scheduled for migration.
• Prepare a rollback plan. Keep the source dataset online for at least a week after migration. Automate fallback if validation fails within that window.
• Instrument everything. Every Honk agent should log its actions in a structured format (JSON logs). This helps debug grey failures.

By following these steps, you can transform a torturous dataset migration into a predictable, automated process. The combination of Honk, Backstage, and Fleet Management gave us the scalability and control we needed—and it can do the same for you.