10 Ways Spotify Streamlined Dataset Migrations with Honk and Backstage

Migrating thousands of datasets across a sprawling microservices architecture is a daunting task. At Spotify, we faced this challenge head-on when we needed to update downstream consumers for a major dataset shift. Our solution leveraged three key tools: Honk, Backstage, and Fleet Management. This listicle breaks down the ten essential strategies we used to turn a painful migration into a smooth, automated process.

1. Understanding the Migration Pain Point

Dataset migrations at scale are notoriously risky. Each dataset has multiple downstream consumers—services that rely on specific schemas, formats, or access patterns. Manually updating each consumer is error‑prone and time‑consuming. We needed a way to track every dependency and coordinate updates without breaking production. The first step was acknowledging this complexity and committing to an automated approach.

10 Ways Spotify Streamlined Dataset Migrations with Honk and Backstage — Source: engineering.atspotify.com

2. Introducing Honk as the Background Agent

Honk is a background agent framework we built at Spotify to handle long‑running, asynchronous tasks. For dataset migrations, Honk agents take on the role of reliable workers. They can be triggered by events, such as a dataset schema change, and then execute a series of steps—validating new schemas, updating consumer code, and testing in sandbox environments. Honk’s built‑in retry logic and state management ensure no migration step is lost.

3. Leveraging Backstage for Dependency Discovery

Backstage serves as our developer portal, providing a unified catalog of all services, datasets, and their relationships. By integrating Backstage’s entity model, we automatically discovered which services consumed a given dataset. This dependency graph became the foundation for our migration plan. Any change to a dataset propagated through Backstage’s metadata, giving us a live inventory of consumers without manual inventory updates.

4. Fleet Management for Orchestrating the Rollout

Fleet Management at Spotify handles the deployment and lifecycle of services across environments. We used it to orchestrate the migration rollout in waves. Instead of updating all consumers simultaneously, Fleet Management allowed us to target groups—starting with low‑risk internal services, then moving to critical production pipelines. This phased approach reduced blast radius and gave us time to catch issues early.

5. Automating the Migration Checklist with Honk Workflows

Each migration step—schema validation, code generation, unit testing, canary deployment—became a stage in a Honk workflow. We encoded the entire checklist as a directed acyclic graph (DAG) in Honk. If a step failed, Honk would pause, alert, and optionally auto‑remediate. This automation eliminated the need for engineers to remember each tedious step, reducing cognitive load and human error.

6. Reducing Manual Toil with Code Generation

When a dataset’s schema changes, downstream consumers often need to update their data‑access code. Instead of asking each team to manually rewrite serializers or queries, we built a code‑generation step into the Honk workflow. The agent would fetch the new schema from Backstage, generate the updated consumer code, and open a pull request. Teams only needed to review and approve, not write from scratch.

7. Implementing Intelligent Retry and Backoff

Not all migration steps succeed on the first try—transient failures from network glitches or stale caches are common. Honk agents include exponential backoff with jitter. If a code generation fails due to a temporary dependency issue, the agent retries after a short delay. After three failures, it escalates to a human via an alert. This balance kept migrations moving without overwhelming ops teams with false positives.

8. Real‑Time Monitoring and Rollback

During the rollout, we used Backstage’s monitoring integration to watch key metrics—error rates, latency, data consistency. Fleet Management provided instant rollback capabilities: if a consumer showed elevated errors after migration, we could revert that service to the previous dataset version in seconds. Honk logged every step, creating an audit trail that helped us pinpoint failures quickly.

9. Scaling the Solution to Thousands of Datasets

By designing Honk agents to be stateless and idempotent, we could run hundreds of migrations in parallel. Backstage’s catalog scaled naturally with our microservices—each new dataset or dependency was automatically discoverable. Fleet Management’s wave‑based scheduling meant we never overwhelmed the infrastructure. Within months, we migrated thousands of datasets without a single major incident.

10. Key Lessons for Future Migrations

Our approach taught us three lessons: First, invest in dependency discovery early—you can’t automate what you don’t know. Second, break migrations into idempotent, testable steps. Third, empower teams with self‑service tools (like Honk and Backstage) rather than centralizing all migration work. These principles now guide every dataset migration at Spotify, turning a once‑dreaded task into a routine operation.

By combining Honk’s background agents, Backstage’s visibility, and Fleet Management’s orchestration, we transformed dataset migrations from a manual, high‑risk endeavor into a predictable, automated process. The result? Faster updates, fewer incidents, and happier engineers. Whether you’re migrating thousands of datasets or just a few, these strategies can help you supercharge your own migration pipeline.

Tags: