Streamlining Large-Scale Dataset Migrations with Honk, Backstage, and Fleet Management

Migrating thousands of datasets across a complex, distributed infrastructure is a daunting task. At Spotify, the engineering teams faced this challenge while transitioning downstream consumer datasets to new storage and processing systems. The solution combined three powerful tools: Honk, Backstage, and Fleet Management. This article explores how these background coding agents supercharged the migration process, reducing pain points and boosting reliability.

The Role of Honk in Automating Migrations

Honk is a specialized system designed to orchestrate dataset migrations at scale. It acts as a background coding agent, automatically generating and executing scripts that transform and move data from source to target systems without human intervention. By abstracting the complexities of schema changes, data consistency checks, and rollback procedures, Honk allowed teams to focus on higher-level migration logic rather than low-level plumbing.

Reducing Manual Effort

Before Honk, each dataset migration required manual coding of transformation scripts, careful scheduling, and extensive testing. With Honk, engineers define migration policies declaratively—specifying the source, target, transformation rules, and validation criteria. Honk then handles the execution, monitoring, and error recovery. This approach dramatically reduced the time spent on repetitive tasks and minimized human error.

Ensuring Data Integrity

One of the biggest risks in dataset migration is data loss or corruption. Honk incorporates built-in checksum verification, comparison of record counts, and incremental validation during the migration. If any discrepancy is detected, the system automatically pauses and alerts the team, enabling quick remediation. This safety net gave engineers confidence to migrate large volumes of data without constant manual oversight.

Leveraging Backstage for Developer Visibility

Backstage, Spotify’s internal developer portal, played a crucial role in providing a unified interface for managing migrations. Instead of digging through logs or dashboards across multiple systems, teams could view the status of all active and completed migrations in a single place. Backstage also served as the command center for triggering new migrations and viewing historical reports.

Tracking Migration Progress

Each migration task was represented as a Backstage entity with rich metadata: source and target dataset names, migration strategy (e.g., copy, transform, merge), current phase (preparation, execution, validation), and error logs. Engineers could filter and search across thousands of tasks, drilling down into specific failures. This transparency helped project managers monitor overall progress and identify bottlenecks in real time.

Self-Service for Teams

Backstage empowered individual teams to initiate and control their own migrations without relying on a central DevOps group. By exposing Honk’s capabilities through Backstage’s plugin framework, engineers could select their datasets, review automatically generated migration plans, and approve execution—all from a web browser. This self-service model reduced coordination overhead and accelerated the migration timeline.

Fleet Management for Distributed Execution

With thousands of datasets to migrate, the execution needed to run across a fleet of machines to parallelize the work. Fleet Management provided the infrastructure for distributing migration tasks across a cluster of worker nodes, handling resource allocation, task scheduling, and failure recovery. It seamlessly integrated with Honk to dispatch tasks based on priority, dependency, and available capacity.

Ensuring Reliability and Rollback

Fleet Management’s built-in retry and dead-letter queue mechanisms ensured that transient failures (e.g., network timeouts, temporary resource exhaustion) did not halt the entire migration. If a task failed repeatedly, it was quarantined for manual inspection. Additionally, the system supported safe rollback by keeping the original dataset intact until the migration was fully validated and committed. This two-phase approach (prepare, validate, commit) minimized data exposure risks.

Scaling with Demand

During peak migration periods, Fleet Management dynamically scaled the worker fleet based on queue depth. This elasticity allowed the migration to complete in hours rather than days, even as new datasets were added to the queue. The combination of Honk’s automation and Fleet Management’s scaling capabilities turned a potentially months-long project into a manageable, parallelized effort.

The Trio in Action: A Real-World Example

Consider a migration of user-playlist data from a legacy Hive cluster to a modern Iceberg table format. Using Honk, the team defined a policy that included schema evolution rules (e.g., adding a new column for playlist description) and data cleaning steps (e.g., removing duplicate entries). Backstage displayed a dashboard showing all 1,500 playlists datasets to be migrated, color-coded by status. Fleet Management scheduled the transformations across 50 parallel workers, each processing multiple datasets sequentially. Validation checks ran automatically after each batch, and the entire migration completed without data loss in under three hours.

Conclusion: Lessons Learned and Future Directions

The combination of Honk, Backstage, and Fleet Management proved to be a winning formula for Spotify’s large-scale dataset migrations. By separating concerns—automation logic (Honk), developer experience (Backstage), and execution infrastructure (Fleet Management)—the engineering team achieved speed, reliability, and transparency. Future improvements may include machine learning-driven anomaly detection during migration validation, tighter integration with data catalog systems, and more granular rollback capabilities. For any organization facing similar migration challenges, adopting a trio of dedicated tools can transform a painful, manual process into an efficient, automated one.