How Jampp architected a real time bidding system on AWS with PrestoDB
In this presentation, Dante and Fede will show Jampp’s data architecture, how they’re training their machine learning algorithms directly from Presto to identify potential purchasers, and why Presto has become 24/7 critical to ensure ads are relevant to users.
Presto for Real Time Analytics at Uber – Ankit Sultana, Uber
The Real Time Analytics Platform at Uber serves 100M+ queries daily and is used for several critical features: from end-user app features to radius selection for Uber Eats. All these queries are proxied via a custom internal fork of Presto (named Neutrino) that is optimized for low-latency/high-throughput (50ms latency at 1000s of RPS). With this talk we plan to share our learnings over the last 6 months and how we run Presto reliably at this scale for real-time analytics.
How Blinkit is Building an Open Data Lakehouse with Presto on AWS – Satyam Krishna & Akshay Agarwal
Blinkit, India’s leading instant delivery service, uses Presto on AWS to help them deliver on their promise of “everything delivered in 10 minutes”. In this session, Satyam and Akshay will discuss why they moved to Presto on S3 from their cloud data warehouse for more flexibility and better price performance. They’ll also share more on their open data lakehouse architecture which includes Presto as their SQL engine for ad hoc reporting, Ahana as SaaS for Presto, Apache Hudi and Iceberg to help manage transactions, and AWS S3 as their data lake.
Realtime Analytics with Presto and Apache Pinot – Xiang Fu
In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this talk, we will explore how to get the best of both worlds using Apache Pinot and Presto: 1. How people do analytics today to trade-off Latency and Flexibility: Comparison over analytics on raw data vs pre-join/pre-cube dataset. 2. Introduce Apache Pinot as a column store for fast real-time data analytics and Presto Pinot Connector to cover the entire landscape. 3. Deep dive into Presto Pinot Connector to see how the connector does predicate and aggregation push down. 4. Benchmark results for Presto Pinot connector.