How and Why Netflix Built a Real-Time Distributed Graph: Part 1 — Ingesting and Processing Data…
Netflix developed a Real-Time Distributed Graph (RDG) to efficiently process and analyze member interactions across devices and services, utilizing a stream processing architecture with Apache Kafka and Flink to handle vast data volumes in real-time.
MAIN POINTS
- Netflix's business evolution necessitated analyzing member interactions across multiple devices and services.
- A Real-Time Distributed Graph (RDG) was built to connect and analyze data instantly.
- Apache Kafka and Flink are used for real-time data ingestion and processing.
- The RDG architecture includes ingestion, storage, and serving layers for efficient data handling.
TAKEAWAYS
- The RDG enables Netflix to create personalized experiences by connecting member activities across platforms.
- Stream processing architecture ensures low latency and up-to-date data representation.
- Kafka topics and Flink jobs are tailored for specific data volumes and processing needs.
- The RDG's flexibility allows for easy adaptation to new data relationships and entities.