Data processing systems have evolved significantly over the last decade, driven by various factors such as the advent of cloud computing, increasingly complexity of applications such as ML, HTAP, Streaming, Observability and Graph processing. However, historically, these frameworks have evolved independently, leading to significant fragmentation of the stack. In this talk, I will talk about how this has evolved in the open source and at Meta, and how we are solving this problem through the Shared Foundations effort, leading to composable systems. This has resulted in significantly better performance, more features, higher engineering velocity and a more consistent user experience.
The Google BigQuery connector gives users the ability to query tables in the BigQuery service, Google Cloud’s fully managed data warehouse. In this presentation, we’ll discuss the BigQuery Connector plugin for Presto which uses the BigQuery Storage API to stream data in parallel, allowing users to query from BigQuery tables via gPRC to achieve a better read performance. We’ll also discuss how the connector enables interactive ad-hoc query to join data across distributed systems for data lake analytics.