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.

Presto on AWS using Ahana Cloud at Cartona – Omar Mohamed, Cartona

Cartona is one of the fastest growing B2B e-commerce marketplaces in Egypt that connects retailers with suppliers, wholesalers, and production companies. We needed to federate across multiple data sources, including transactional databases like Postgres and AWS S3 data lake. In this session, we’ll talk about how Presto allows us to join across all of these data sources without having to copy or ingest data – it’s all done in place. In addition, we’ll talk about how we were up and running in less than an hour with the Ahana Cloud managed service. It gives us the power of Presto and the ease of use without the need to manage it or have deep skills to deploy and operate it.

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.