Accelerating Netflix Data: A Cross-Team Journey from Offline to Online

The presentation titled Accelerating Netflix Data: A Cross-Team Journey from Offline to Online discusses the significant transformation of Netflix's data infrastructure from offline to online systems. The presentation highlights the collaborative effort and innovative strategies used to enhance data deployment efficiency.

• Introduction

  The presentation begins with an introduction about the need for efficient data deployment in Netflix's backend, to meet consumer demands and acceptable latency requirements. The shift from offline pipelines to a data abstraction layer was essential to improve system performance and manage data better.

• Key Concepts
  • Cross-functional collaboration was crucial in defining requirements, overcoming challenges, and implementing solutions seamlessly.
  • The transformation led to a 99% reduction in data deployment time, along with a 70% cost saving.
  • The project necessitated rethinking architecture and core principles, aligning diverse stakeholders, and adapting prototypes rapidly.
• Innovative Solutions
  • An architectural pivot involved transforming large datasets from offline to online serving systems using a framework called "capture, conversion, deployment."
  • The solution optimized data formats for deployment and integrated mechanisms for safety, observability, and validation.
  • Implemented a key-value abstraction to provide a stable interface for accessing databases, which minimized the need for direct application-level interventions.
• Challenges and Strategies
  • Identifying and exploiting access patterns and aligning stakeholder expectations was key to the project's success.
  • Overcoming performance bottlenecks through strategic handling of massive data sets and leveraging cloud infrastructure effectively.
• Outcomes and Future Directions
  • The transition enabled faster data set provisioning, improved ML platform development, and facilitated robust business innovations.
  • Future aims include extending methodologies to mutable data sets and optimizing deployment processes for various data stores.
• Conclusion

  The presentation concludes by emphasizing the importance of confidence and safety in technological transitions, and highlights the role of abstraction in achieving seamless, large-scale migrations.

This is the end of the AI-generated content.

At Netflix, certain use cases demand the rapid transfer of massive datasets—such as 50 TB—from offline to online systems. Doing this efficiently, without disrupting applications interacting with our online systems, presents a significant challenge. Traditional data transfer methods, such as using batch processing systems and loading data into online systems via PUT APIs, posed significant scalability and cost hurdles, often leading to performance bottlenecks and impacting system efficiency. To overcome these limitations, an innovative architectural solution was developed. This approach involved transforming offline data into an optimized format through pre-processing, staging this data, transforming offline data into RocksDB SST file format, staging these files in the cloud, and enabling direct, on-demand ingestion into the serving system.

This process necessitated navigating complex internal discussions and aligning diverse stakeholders on new technical strategies. It also required rapidly adapting initial prototypes to address urgent customer needs by initially prioritizing speed to onboard them to the prototype, before shifting efforts towards building a robust, scalable production system. Crucially, cross-functional collaboration proved essential. Teams from various domains worked closely to define requirements, overcome challenges, and ensure seamless implementation.

Ultimately, this collaborative effort led to the successful deployment of a system that provides enhanced performance, reducing data deployment time by 99% (from days to just 30 minutes) and cutting costs by 70%. This presentation will delve into the journey of transforming data pipelines at scale, highlighting the key technical strategies, strategic decisions, and crucial team efforts that made this significant improvement possible.

Key Takeaways:

• Discover the challenges and solutions for large-scale data movement from batch storage to online serving systems.
• Understand the innovative architectural approach to improve data deployment efficiency.
• Strategic decision-making and problem-solving in a high-pressure environment.
• The importance of cross-functional team collaboration in solving complex engineering problems.