Accelerating Data Migrations: How Background Coding Agents Transform Dataset Transfers at Spotify

Migrating thousands of downstream consumer datasets is a daunting task, often fraught with manual effort, risk of errors, and significant downtime. At Spotify, we tackled this challenge by leveraging background coding agents powered by Honk, Backstage, and Fleet Management. These tools work together to automate, coordinate, and scale migrations, supercharging the process and easing the pain for engineering teams. Below, we answer key questions about this innovative approach.

What are background coding agents and why are they needed for dataset migrations?

Background coding agents are automated processes that handle the tedious, repetitive tasks involved in migrating datasets between systems. Instead of relying on engineers to manually rewrite code or update configurations for each dataset, these agents act as intelligent helpers that can analyze, transform, and move data in the background. They are essential because downstream consumer dataset migrations—like those Spotify handles—involve thousands of individual datasets, each with unique schemas, dependencies, and consumption patterns. Manual migration would be incredibly slow, error-prone, and resource-intensive. By deploying background coding agents, we can run these migrations automatically, freeing engineers to focus on higher-level architecture decisions while ensuring consistency and accuracy across the board.

Accelerating Data Migrations: How Background Coding Agents Transform Dataset Transfers at Spotify — Source: engineering.atspotify.com

How does Honk play a role in the migration process?

Honk is a core component that provides the orchestration framework for background coding agents. Think of Honk as the conductor of an orchestra, coordinating when and how each agent executes its migration tasks. It manages the lifecycle of agents, including scheduling, monitoring, and error handling. Honk also integrates with Backstage to discover which datasets exist, their current coding state, and their downstream consumers. When a migration is triggered, Honk dispatches agents to analyze each dataset’s codebase and automatically generate the necessary migration scripts. This eliminates the need for manual inspection and reduces the likelihood of human error. Honk’s built-in retry and logging mechanisms ensure that even if an agent fails temporarily, the migration can resume without data loss or corruption.

What is the role of Backstage in coordinating migrations?

Backstage serves as the central catalog and metadata store for all software components at Spotify, including datasets. During migrations, Backstage provides the inventory of every dataset that needs to be moved, along with critical metadata such as owners, dependencies, and compliance tags. The background coding agents query Backstage to understand the full picture before acting. For example, an agent can check if a dataset is actively used by multiple downstream consumers, and if so, plan a migration that minimizes disruption. Backstage also tracks the state of each migration—whether a dataset has been migrated, is in progress, or is blocked. This visibility is crucial for engineering leads to monitor progress and identify bottlenecks. By integrating with Honk, Backstage enables a self-service migration experience where teams can initiate mass migrations with just a few clicks.

How does Fleet Management help manage the scale of migrations?

Fleet Management handles the operational aspects of running background coding agents across thousands of datasets. Since each migration may involve spinning up temporary compute resources, Fleet Management ensures that the necessary capacity is available and efficiently utilized. It prevents resource contention by scaling agents up or down based on demand, and it can prioritize critical migrations over less urgent ones. For instance, if a data store is being decommissioned, Fleet Management can allocate more agents to migrate those datasets first. Additionally, Fleet Management monitors the health of agents and automatically replaces any that become unresponsive. This layer of automation is what allows Spotify to migrate tens of thousands of datasets in parallel without overwhelming the underlying infrastructure. Without Fleet Management, the sheer scale of concurrent migrations would be unmanageable manually.

What benefits have Spotify engineers observed from this approach?

Engineers at Spotify have reported several significant benefits from using background coding agents with Honk, Backstage, and Fleet Management. First, the time required for a full migration dropped from months to days, as agents work 24/7 without human intervention. Second, error rates plummeted because agents follow strict templates and validation steps, reducing the chance of misconfigurations. Third, the process became more transparent—Backstage dashboards allow everyone to see exactly which datasets have been migrated and which remain. Fourth, teams gained the ability to run dry-run migrations to test without affecting production, catching issues early. Finally, the approach freed up engineers to focus on feature development rather than tedious data plumbing. One engineer noted, “We used to dread dataset migrations. Now, we just set it and forget it.”

Can these techniques be applied to other large-scale infrastructure changes?

Absolutely. While the example at Spotify focuses on dataset migrations, the underlying pattern of background coding agents, orchestrated by a tool like Honk, informed by a catalog like Backstage, and scaled via Fleet Management, is broadly applicable. Any large-scale infrastructure change that involves repetitive, pattern-based transformations—such as migrating microservices to a new framework, upgrading database schemas, or shifting to a different cloud provider—can benefit from this approach. The key is to invest in capturing metadata (Backstage), building agents that can automate the coding changes (Honk), and ensuring robust capacity management (Fleet Management). Organizations facing multi-thousand node migrations or similar challenges should consider adopting these practices to reduce manual toil and accelerate their technical evolution.

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