Streamlining Large-Scale Dataset Migrations with Automated Background Agents

Introduction

Migrating thousands of datasets across environments is a daunting task. Manual processes are error-prone, slow, and resource-intensive. At Spotify, we faced this challenge head-on by building a pipeline using three powerful tools: Honk, Backstage, and Fleet Management. Honk handles the background coding agents that execute migration tasks, Backstage serves as the developer portal for cataloging and managing these tasks, and Fleet Management orchestrates the deployment and scaling of agents across the infrastructure. This guide walks you through the exact steps we used to automate dataset migrations, reduce downtime, and maintain data integrity at scale.

What You Need

• Honk setup – A running instance of Honk configured with background coding agents. Honk must be able to receive and process migration jobs.
• Backstage instance – A working Backstage portal with custom plugins for cataloging datasets and monitoring migration workflows.
• Fleet Management platform – A service to manage agent fleets, such as Kubernetes or a dedicated Fleet Management tool like Fleet (by Spotify). This controls agent deployment, scaling, and health.
• Source and target dataset systems – Databases, data lakes, or storage systems (e.g., HDFS, S3, BigQuery) containing the datasets to migrate.
• Migration specifications – A list of datasets with their source locations, target locations, transformation rules, and schema mappings.
• Access and credentials – Service accounts or API keys with read/write access to both source and target systems.
• Monitoring and logging tools – Stackdriver, Prometheus, or similar for tracking migration progress and errors.
• CI/CD pipeline – Git-based workflow (e.g., GitHub Actions) to trigger migrations automatically.

Step-by-Step Guide

Step 1: Catalog Datasets in Backstage

First, you need to inventory every dataset that requires migration. Use Backstage’s custom entity provider to register each dataset as a component. For each dataset, define metadata such as source URI, target URI, schema version, retention policy, and migration priority. Attach tags like migration:true so they appear in migration dashboards. Backstage’s search and filtering capabilities allow teams to quickly identify and group datasets by owner, region, or compliance requirements.

Step 2: Define Migration Jobs as Honk Tasks

Honk expects tasks in a structured JSON schema. Create a task for each dataset or batch of datasets. Each task includes:

• A unique ID referencing the Backstage entity.
• Source connection details and target connection details.
• Transformation logic (e.g., SQL scripts, Python transformation functions).
• Error handling rules (retry count, dead-letter queues).

Use Honk’s API to register these tasks. For large migrations, generate tasks programmatically from the Backstage catalog using a simple script that reads the dataset list and populates the task template.

Step 3: Deploy Background Coding Agents via Fleet Management

Fleet Management is responsible for running Honk agents at scale. Configure a fleet of agents that each poll for pending Honk tasks. In Fleet Management, define an agent blueprint:

• Base container image with Honk client library installed.
• Environment variables for Backstage API endpoint and credentials.
• Resource limits (CPU, memory) based on dataset size.

Launch the fleet with a desired count of agents. Fleet Management handles auto-scaling: if the task queue in Honk grows, it spins up more agents; if idle, it shrinks the fleet. This ensures cost-efficient utilization.

Step 4: Initiate Migration from Backstage

Now trigger the actual migration. In your Backstage plugins, add a “Start Migration” action button to each dataset entity. When clicked, this action calls Honk’s API to enqueue the corresponding task. You can also create bulk migration workflows: select multiple datasets, define a migration window (e.g., “run after midnight”), and submit a batch job. Backstage shows real-time status: pending, running, success, failed.

Step 5: Monitor Agents and Dataset Transfers

As agents execute tasks, you need end-to-end visibility. Each agent reports its progress back to Honk, which in turn updates the Backstage entity status. Set up a Fleet Management dashboard to monitor agent health – CPU, memory, network I/O, and error rates. Additionally, stream logs from agents into a centralized logging system (e.g., Cloud Logging) and create alerts for critical failures, like authentication errors or schema mismatches.

Step 6: Handle Failures and Reruns

Migrations rarely go perfectly. When a task fails, Honk automatically retries based on the error rules. For persistent failures, the task is moved to a dead-letter queue. Fleet Management should pause the agent and record the failure in Backstage. A team member can then investigate the dataset, fix the issue, and re-enqueue the task through Backstage. To avoid data corruption, implement a rollback mechanism: before each migration, take a snapshot of the source dataset. If the migration fails and data integrity is compromised, the agent should revert the target to the previous state.

Step 7: Validate and Sign Off in Backstage

After a dataset migration succeeds, validation is critical. The agent runs a post-migration check: compare row counts, checksums, or sample data between source and target. If validation passes, the agent updates the Backstage entity to migrated:true and adds a timestamp. Your team can then perform manual spot checks, but automated validation reduces the burden. Once validated, the dataset is considered complete and can be retired from the source system.

Step 8: Scale and Iterate

With the core pipeline in place, you can scale to thousands of datasets. Use Backstage to create priority queues: critical datasets (e.g., those with compliance deadlines) migrate first. Fleet Management can be configured to run agents in different geographic regions to minimize latency. Regularly review migration logs and update Honk tasks to handle edge cases. This process is not one-time – new datasets are added to Backstage, and the pipeline keeps running in the background.

Tips for Success

• Start small – Test with a handful of low-risk datasets first to validate the pipeline before scaling to thousands.
• Use idempotent tasks – Design Honk tasks so they can be safely retried without causing duplicate data or inconsistencies.
• Monitor agent resource usage – Fleet auto-scaling works well, but set explicit maximums to avoid overwhelming the source/target systems.
• Keep Backstage metadata current – Always update dataset ownership and schema in Backstage before migration to avoid permissions errors.
• Document migration rules – Maintain a changelog of transformations and exceptions so your team can reuse them for future migrations.
• Schedule migrations during low-traffic windows – Use Honk’s delayed enqueue or cron triggers to run bulk tasks overnight or on weekends.
• Integrate with CI/CD – Push migration job definitions as code into version control so changes are reviewed and tested.

By following these steps, you can transform a painful manual migration into a smooth, automated process. Honk, Backstage, and Fleet Management work together to give you control, visibility, and reliability – even when migrating tens of thousands of datasets.