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Automating Large-Scale Dataset Migrations with Background Coding Agents: A Practical Guide

Last updated: 2026-05-17 04:39:25 · Environment & Energy

Introduction

Migrating thousands of datasets across systems can be a daunting task. Teams often face downtime, broken downstream consumers, and manual errors that slow progress and frustrate developers. At Spotify, we tackled this challenge by combining three powerful tools: Honk (our background coding agent framework), Backstage (our developer portal for cataloging and managing services), and Fleet Management (for orchestrating containerized workloads). Together, these components form a robust pipeline that automates dataset migrations with minimal human intervention. This guide walks you through creating your own background coding agents to supercharge downstream consumer dataset migrations—reducing pain and increasing reliability.

Automating Large-Scale Dataset Migrations with Background Coding Agents: A Practical Guide
Source: engineering.atspotify.com

What You Need

  • Access to a Kubernetes cluster (or similar container orchestration platform) to deploy Fleet Management agents.
  • Backstage instance with an up-to-date software catalog containing your dataset definitions and consumer service metadata.
  • Honk runtime environment – if Honk is not available, you can replace it with any workflow engine that supports event-driven triggers (e.g., Apache Airflow, Argo Workflows).
  • Source dataset repositories (like SQL tables, data lakes, or object stores) with version control or change detection.
  • Target storage systems (e.g., new database, file format, or cloud service) where migrated datasets will reside.
  • Downstream service names and API endpoints – these are the consumers that rely on your datasets and must be updated after migration.
  • Basic familiarity with YAML, JSON, and your preferred scripting language (Python is recommended).

Step-by-Step Guide

Step 1: Map Your Dataset Ecosystem in Backstage

Start by registering all datasets and their downstream consumers in your Backstage catalog. Use the built-in entity types for "Dataset" and "Service". For each dataset, define metadata: name, location, schema version, owning team, and criticality. For each downstream service, link it to the datasets it consumes. This creates a dependency graph that your background agents will query later.
Tip: Use Backstage’s catalog-info.yaml to automate registration. For example:

apiVersion: backstage.io/v1alpha1
kind: Component
metadata:
  name: my-dataset
  annotations:
    backstage.io/view-url: https://dataplatform/datasets/my-dataset
spec:
  type: dataset
  lifecycle: production
  owner: team-alpha

Step 2: Set Up Honk for Event-Driven Triggers

Honk acts as the brain of the operation. Configure Honk to listen for migration events—for example, when a dataset change is merged to its repository, or when a scheduled migration window opens. Create a Honk agent script that will orchestrate the entire migration workflow. The script should:

  • Receive the trigger payload (e.g., dataset ID, new schema, or target storage type).
  • Query Backstage for all downstream services using fetch from the Backstage API.
  • Check the current health status of those services. Skip any unhealthy services to avoid cascading failures.

Step 3: Build the Migration Workflow

In your Honk agent, define the migration steps as a directed acyclic graph (DAG). Typical steps:

  1. Extract: Read the source dataset in chunks to avoid memory overload. Use parallel workers if needed.
  2. Transform: Apply any schema changes, data type conversions, or enrichment (e.g., adding timestamps).
  3. Load: Write the transformed data to the target location. Use idempotent writes so that retries won’t duplicate data.
  4. Validate: Run integrity checks: record count, checksum, and sample queries. Fail fast if mismatches occur.
  5. Notify: Send alerts to the owning team and downstream service owners via Backstage’s notification system or Slack.

Each step should log detailed metrics (duration, rows processed, errors). Honk can emit these to your monitoring stack.

Step 4: Deploy Fleet Management Workers

Large migrations demand horizontal scalability. Use Fleet Management to deploy Honk agents as containerized jobs across your Kubernetes cluster. Configure auto-scaling based on queue depth. For example, if 50 datasets are queued for migration, spin up 10 workers. Each worker picks a dataset from the queue, executes the workflow (Step 3), and reports completion.
Important: Ensure each worker has read/write access to both source and target storage. Use Kubernetes secrets for credentials.

Automating Large-Scale Dataset Migrations with Background Coding Agents: A Practical Guide
Source: engineering.atspotify.com

Step 5: Update Downstream Consumers

After a dataset is successfully migrated and validated, your Honk agent must inform Backstage. Update the dataset entity’s location to point to the new target. Then, via Backstage’s API, trigger a “reconnection” event for each consuming service. Those services can then automatically switch their data source references. For non-automated services, generate a pull request that updates their configuration files, and attach the Honk agent’s validation report as evidence.

Step 6: Monitor and Rollback

Build a monitoring dashboard in your observability platform (e.g., Grafana, Datadog) showing:

  • Number of datasets migrated per hour
  • Success/failure rates
  • Downstream consumer health after migration
  • Queue depth in Fleet Management

Define a rollback procedure: if a downstream consumer reports issues within 30 minutes, revert the dataset location in Backstage and repoint to the old source. Run a clean-up Honk job to delete the new target if rollback is triggered.

Step 7: Iterate and Optimize

After your first batch migration, review logs and metrics. Optimize transformation logic for slow datasets, increase chunk sizes for faster loads, and polish error handling. Use Honk’s built-in retry mechanism with exponential backoff to handle transient failures. Finally, write unit tests for your Honk agent scripts and run them in a staging environment before each major migration wave.

Tips for Success

  • Start small: Pilot with a few low-priority datasets before tackling thousands. This helps you validate your Honk agent and Fleet Management scaling without risking critical services.
  • Use canary deployments: Migrate one dataset at a time for a subset of consumers, monitor for regressions, then roll out to all.
  • Keep Backstage metadata clean: Outdated or missing downstream service entries can cause your Honk agent to skip important consumers. Schedule regular audits of your catalog.
  • Leverage idempotency: Design every migration step so that running it multiple times yields the same result. This simplifies debugging and allows safe restarts.
  • Communicate proactively: Notify all downstream service owners before and after migrations. Use Backstage’s built-in announcements or integrate with your team’s chat tool.
  • Budget for unexpected delays: Always allocate extra time per migration (e.g., 20% overhead) to account for validation, rollbacks, and coordination.
  • Document your pipeline: Create a runbook in Backstage’s tech docs plugin that describes the migration process, including how to manually trigger Honk agents and how to recover from failures.

With background coding agents coordinating through Backstage and powered by Fleet Management, you can transform a painful manual process into a reliable, automated system. Your downstream consumers will experience less downtime, your teams will spend less time firefighting, and you’ll gain confidence to migrate at scale.