Videos Archive - PrestoDB

Shared Foundations Of Composable Data Systems – Biswapesh Chattopadhyay, Google

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 Future of Presto’s Query Optimizer – Bill McKenna, Ahana

In this talk, you will hear from the query optimizer OG himself, Bill McKenna (Principal software engineer at Ahana, Architect for the query optimizer that became the code base of the Amazon Redshift query optimizer, and co-author of The Volcano Optimizer Generator: Extensibility and Efficient Search) go into detail about the state of modern query optimizers, and how Presto stacks up against them and where it will go in the near future. If database theory is your jam, you won’t want to miss this deeply technical presentation from one of the pioneers in the field.

Using Presto’s BigQuery Connector for Better Performance and Ad-hoc Query connector for better performance and ad-hoc query in the Cloud – George Wang & Roderick Yao

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.