AWS re:Invent 2016: Content and Data Platforms at Vevo: Rebuilding and Scaling from Zero (SVR308)

Опубликовано 3 декабря 2016, 2:02

Vevo has undergone a complete strategic and technical reboot, driven not only by product, but also by engineering. Since November 2015, Vevo has been replacing monolithic, legacy content services with a modern, modular, microservices architecture, all while developing new features and functionality. In parallel, Vevo has built its data platform from scratch to power internal analytics as well as a unique music video consumption experience through a new personalized feed of recommendations — all in less than one year.

This has been a monumental effort that was made possible in this short time span largely because of AWS technologies. The content team has been heavily using serverless architectures and AWS Lambda in the form of microservices, taking a similar approach to functional programming, which has helped us speed up the development process and time to market. The data team has been building the data platform by heavily leveraging Amazon Kinesis for data exchange across services, Amazon Aurora for consumer-facing services, Apache Spark on Amazon EMR for ETL + Machine Learning, as well as Amazon Redshift as the core analytics data store.

In this session, Miguel and Alan walk you through Vevo's journey, describing best practices and learnings that the Vevo team has picked up along the way.