Flink and Kafka are leveraged to for features such as personalized recommendations
Our mission is to "Entertain the World". As pioneers of the streaming video-on-demand model, we revolutionized how people consume entertainment. Our vast library offers a diverse array of original movies, TV series, documentaries, and more. Driven by innovation, Netflix has expanded into new business verticals, including games, advertising, and live streaming.
At Netflix, we have built sophisticated platforms on top of Kafka and Flink to democratize real-time data. These platforms empower software engineers, data practitioners, and domain experts to rapidly and effortlessly create data pipelines that support a diverse array of use cases. Furthermore, for operational high-cardinality use cases, we built and open sourced Mantis; a real time stream processing platform for ingesting and processing operational data cost effectively.
A key design principle of these platforms is to alleviate as much operational overhead as possible from the end-users, allowing them to focus on their core responsibilities and leverage the power of real-time data without being burdened by the complexities of managing the underlying infrastructure.
This user-centric approach has facilitated widespread adoption of these platforms, resulting in the deployment of tens of thousands of Kafka topics and Flink jobs in production environments today.
Data Streaming Technology Used:
Apache Kafka, Apache Flink, Confluent Schema Registry, Netflix Mantis (https://github.com/netflix/mantis)
What problem were they looking to solve with Data Streaming Technology?
At Netflix, we need to process petabytes of data every day in order to support our diverse business lines. This workload is handled by tens of thousands of Flink jobs and Kafka topics distributed across hundreds of Kafka clusters. Operating a system of such scale and complexity would be an operational nightmare without dedicated management solutions. At this scale, infrastructure costs can quickly become untenable. Consequently, we must ensure that our jobs are autoscaled optimally and our clusters are sized appropriately to maximize cost efficiency.
How did they solve the problem?
Netflix has developed and deployed fully managed platforms for Flink and Kafka, empowering engineers across the organization to rapidly build and deploy real-time data pipelines. These platforms are seamlessly integrated with Netflix's infrastructure, providing dedicated support and enabling engineers to operate these pipelines in production environments with confidence. By optimizing these platforms at scale, Netflix has achieved significant cost efficiency gains and reduced operational burden.
Furthermore, Netflix has developed abstraction products built on top of the Flink and Kafka platforms, enabling no-code/low-code creation of data pipelines. This innovative approach has significantly lowered the barrier to entry, expanding the potential user base beyond engineering and data practitioners.
What was the positive outcome?
Netflix's data streaming platforms are mission-critical components that enable key functionalities across the company's product portfolio. For the core video streaming experience, Flink and Kafka are leveraged to deliver essential features such as personalized recommendations, multi-household accounts, and billing systems. The streamlined architecture and user-friendly interfaces of these platforms have facilitated the rapid deployment of new business verticals, including gaming and advertising. As Netflix expands into live video streaming, real-time data processing capabilities have become increasingly crucial for maintaining operational visibility and ensuring seamless service delivery.
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