How to Automate Large-Scale Dataset Migrations Using Background Coding Agents and Fleet Management

Introduction

Migrating thousands of datasets across a distributed infrastructure is a daunting task. Manual efforts are error-prone, slow, and unsustainable. At Spotify, we tackled this challenge by combining three powerful tools: Honk (a background job platform), Backstage (a developer portal for cataloging), and Fleet Management (for coordinating execution across nodes). This guide walks you through the step-by-step process of setting up background coding agents to supercharge your downstream consumer dataset migrations, reducing pain and increasing reliability.

How to Automate Large-Scale Dataset Migrations Using Background Coding Agents and Fleet Management — Source: engineering.atspotify.com

What You Need

Honk: A background job execution framework (or similar system) to run migration tasks asynchronously.
Backstage: An instance of Backstage or equivalent developer portal to define and catalog datasets and their migration status.
Fleet Management: A tool to orchestrate multiple worker nodes (e.g., Kubernetes, Nomad, or custom fleet scheduling).
Dataset definitions: Clear specifications of source and target schemas, transformation logic, and migration scripts.
Migration scripts: Code (e.g., Python, Java) that performs the actual data transformation and movement.
Access to your data storage: Permissions to read source data and write to target destinations.
Monitoring and logging infrastructure: e.g., Prometheus, Grafana, or ELK stack to track job progress and errors.
CI/CD pipeline: Optional but recommended to deploy new migration scripts without manual intervention.

Step-by-Step Guide

Step 1: Define Dataset Migration Requirements in Backstage

Start by cataloging all datasets that need migration in Backstage. For each dataset, record metadata like source location, target format, transformation rules, and priority. Use Backstage’s entity model to create custom components or resources representing datasets. This catalog becomes your single source of truth for the migration effort.

Create a Backstage plugin or extend an existing one to register datasets.
Add fields for migration status (e.g., pending, in progress, completed, failed).
Tag datasets with owner, criticality, and dependencies to schedule migrations intelligently.

Step 2: Set Up Honk for Background Job Execution

Honk is your background agent that runs migration tasks. Deploy Honk workers on your fleet and configure them to pull jobs from a queue. Each migration script becomes a Honk job that can be triggered manually or automatically via API.

Install Honk (if self-hosted) or use a managed service.
Define job types: one per dataset or one generic job parameterized by dataset ID.
Set up retry logic, timeouts, and concurrency limits to avoid overloading systems.

Step 3: Create Migration Scripts as Honk Jobs

Write modular migration scripts that Honk can execute. Each script should handle a single dataset or a batch of related datasets. Ensure scripts are idempotent – running them multiple times produces the same result without side effects.

Use a language supported by Honk (e.g., Python).
Include error handling and detailed logging.
Store scripts in version control and deploy via your CI/CD pipeline.
Parameterize the script to accept dataset IDs, source/target paths, and transformation options.

Step 4: Use Fleet Management to Coordinate Across Nodes

Fleet Management allocates Honk workers to specific nodes and ensures they have the right resources (CPU, memory, network). This step is critical when migrating thousands of datasets in parallel across multiple data centers or cloud regions.

Define worker groups based on dataset priority or geographic location.
Use Fleet Management's scheduling policies to distribute jobs evenly and respect node capacity.
Integrate with Honk via hooks so that new jobs are automatically routed to available workers.

Step 5: Run and Monitor Migrations

Trigger the migration jobs from Backstage or Honk’s dashboard. Monitor progress in real time using your logging tools. Honk provides job statuses (queued, running, succeeded, failed). Correlate with dataset metadata in Backstage to see which datasets are done.

Set up alerts for job failures or slow progress.
Use dashboards to track overall migration completion percentage.
Implement manual approval gates for critical datasets (e.g., after a successful run, require sign-off before marking as complete).

Step 6: Verify and Rollback if Needed

After a migration completes, verify the new dataset’s integrity. Compare record counts, checksums, or sample queries between source and target. If something is wrong, use your rollback strategy – either rerun an inverse migration or restore from backup.

Automate verification as a post-migration Honk job.
Document rollback procedures in Backstage for each dataset.
Update Backstage status to 'verified' only after successful checks.

Tips for a Smooth Migration

Start small: Test with a handful of low‑priority datasets before scaling to thousands.
Ensure idempotency: Scripts should be safe to re‑run – check if data already exists before writing.
Log everything: Capture job inputs, outputs, errors, and timestamps. Store logs centrally for debugging.
Use incremental migrations: If possible, migrate data in batches to reduce impact on source systems.
Monitor resource usage: Fleet Management can throttle jobs if cluster resources are constrained.
Involve dataset owners: Have them verify their own data post‑migration using the Backstage catalog.
Plan for rollback: Always have a plan to revert changes quickly if issues arise.
Automate everything: From job creation to status updates – reduce manual steps to avoid errors.

By following this guide, you can transform a painful manual migration into an automated, scalable process powered by background coding agents. The combination of Honk for execution, Backstage for visibility, and Fleet Management for coordination provides a robust foundation for even the largest dataset migrations.

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