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Netflix Platform Data Technologies Vision
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Mission and Vision of Netflix's Platform Data Technologies Team
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Netflix Platform Data Technologies Vision
1.
Mission The Platform Data Technologies team has a broad mandate of enabling Netflix developers to effortlessly build and manage systemtosystem sharing of structured data. Our goal is to maximize engineering velocity in the Idea→POC→PROD process. We strive to minimize the development and operation costs of adopting our infrastructure and create strong relationships with all Netflix engineering teams to better understand and address their painpoints and undiscovered common needs. While improving the productivity of engineering teams is a major focus, we also want to continually invest in raising the bar in our own team by hiring the best and enabling personal growth and technology crosslearning while reducing the OnCall load. History Our team was initially formed to develop technologies for rapid, efficient, and reliable distribution of Netflix’s video catalog to all midtier systems. Innovations in this area proved to be horizontal in nature, such as Versioned Pub/Sub and Netflix Open Source Hollow . In turn, interactions with our diverse teams helped identify highly leverageable possibilities that led us to investing in our DataasaService Framework and Unified Logging. Technology Portfolio Catalog We provide the infrastructure to aggregate a massive amount of video metadata from many studiofacing systems, create a unified API, and deliver roundtheclock updates to all Netflix services in a highly optimized and timely manner. Since this data affects every aspect of a user’s experience, from user interface to personalized recommendations, streaming, A/B testing, and even content caching at ISP locations, it’s critical that our system is highly available, accommodates continuous data updates and yet minimizes and mitigates the risk of bad data affecting downstream systems. DataasaService (DaaS) Framework Many teams create handcrafted systems to publish data to other systems but this always involves a nontrivial amount of time and effort to harden, operationalize, and evolve. Our goal was to enable creation of an endtoend fully operationalized DaaS system in about an hour! That’s as much energy as we want teams to spend dealing with the mechanics of reliably moving large structured data between systems. Our infrastructure generates the client APIs, dashboards,
2.
metrics, logging, simplifies adding validation rules and circuit breakers, and more, so every team doesn’t have to! The technologies and learnings from the Netflix Catalog system can now be applied to any dataset and increase the velocity and reliability of all DaaS systems at Netflix. Largepayload Versioned Pub/Sub Everyone is familiar with common Pub/Sub mechanisms like Kafka that publish events to downstream consumers. However, publishing a large and changing dataset to multiple cloud regions will need you to rollout your own solution. We observed this pattern in many Netflix services and implemented a common infrastructure that does the heavy lifting and exposes a simple and familiar Pub/Sub model. This widely adopted solution also provides consumer startup resiliency via fallback mechanisms in case of unavailable datasets that a system depends on. Unified Logging Netflix systems create a substantial amount of logs across multiple tech stacks that are needed to help diagnose production systems. Over the years many solutions were used across the company with varying degrees of universality, developerfriendliness, query latency, and cost characteristics. After examining the ecosystem and speaking with many engineering teams, it became clear that they would benefit greatly from a single mechanism that’s easy to adopt, scalable, cost effective, tunable for a team’s specific needs, extensible, and integrated into Netflix’s cloud infrastructure. We’re still in the early stages of this project but plan to leverage proven open source and Netflix technologies for a great developer experience that minimizes the meantimetodiagnosis of problems and delivers superior ROI. Some of the challenges Small hiccup → Huge impact With over 125M global subscribers and continuously increasing viewing hours, even shortduration mishaps create a bad user experience and increase the OnCall load. As a Platform team, we want our infrastructures to help teams minimize the frequency and blast radius of common issues. We all use techniques to prevent bad code affecting system availability but less is usually done to systemically prevent bad data poisoning the ecosystem. Unvetted data changes are at least as risky as code changes so our team continuously invests in building and improving technologies and bestpractices that avoid many customerimpacting issues. We routinely incorporate these innovations into our infrastructure, make them simple to adopt, and champion this critical aspect of system development throughout the company.
3.
Heterogeneous Ecosystem There’s a general pattern in many dataflows: These flows frequently cross dissimilar technology stacks and engineering teams with various data hygiene, correctness, and backwardcompatibility practices. While data used to primarily flow from midtier systems to each other and to the data warehouse for analytics purposes, it now increasingly flows back from the data warehouse to midtier systems as well to provide a powerful dynamic feedback loop. Our team will need to embrace this macro trend and help increase the velocity of building, operationalizing, and improving availability of all dataheavy systems at Netflix regardless of their tech stack. Invest, Innovate, Iterate The continual evolution of Netflix’s business, increasing scale, and proliferation of datacentric services drive us to keep improving and expanding our infrastructure portfolio to stay ahead of the game. Our technologies are at various maturity levels and need different types and amounts of investment. We must investigate promising open source projects for our tech stack, optimize the operational needs of more mature systems through automation, rearchitect to increase scalability, streamline APIs and harden newer systems, validate and iterate on MVP feature sets, help rapidly migrate teams from older infrastructure or custom implementations to new, generalized, and more robust solutions, improve usability of our tools, and much much more. With so many possibilities, we must be very judicious in making the right level of investments in each of our technologies through thoughtful evaluation, debating priorities, gathering feedback from other teams, and applying good judgement. Our guiding principle in making these decisions is simply “What’s the best thing for Netflix”.
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