Data @Scale 2017 Recap

This post summarizes presentations from the Data @Scale 2017 conference, detailing advancements in several key data engineering areas. It covers the evolution of globally-distributed databases (Microsoft Azure), interactive analytics DBMS like ClickHouse (Yandex), machine learning serving platforms (Pinterest), micro-service architectures beyond request/reply with Cadence (Uber), platforms for metrics computation and experimentation (LinkedIn's UMP and XLNT), the evolution of globally distributed data management systems like Spanner (Google), and Facebook's system for bulk data movement across global storage. It also touches upon accelerating machine learning with infrastructure design and the use of programmable hardware in cloud specialization.