What does it take to safely delete data at Netflix scale? In large-scale systems, data deletion cuts across infrastructure, reliability, and performance complexities. Data lives in many datastores, each with different trade-offs and requiring ad-hoc solutions, leaving users with fragmented behavior, inconsistent outcomes, and costly operational overload. As data volumes grow and data stores become increasingly distributed and complex, ensuring safe deletion becomes an even greater challenge. Without a centralized architecture, teams often develop isolated solutions, resulting in inconsistent practices, duplicated effort, and growing operational overhead.

At Netflix, we have developed an architecture for managing data deletion across diverse data stores, addressing these challenges while improving overall system resilience. The centralized and extensible platform provides the end-to-end data deletion lifecycle from identifying the data to verifying and executing deletion. The platform includes configurable deletion controls, journaling, observability, and data recoverability to ensure safe and reliable operation.

In this talk, we share the design and execution tradeoffs behind the data deletion platform. We explain how we have used various techniques to build a reliable and auditable deletion system, and we highlight key engineering tradeoffs, including how we balance throughput, safety, and scalability across diverse systems while maintaining resilience under live traffic.

Key Takeaways:

• Understand the architectural challenges of data deletion and why a centralized approach is essential.
• Learn how orchestration, observability, journaling, and recoverability enable safe deletion across diverse data stores.
• Explore the tradeoffs Netflix made to balance throughput, safety, and scalability under live traffic.
• Gain practical insights from real-world engineering decisions in building and operating large-scale deletion workflows.