Videos Archive - PrestoDB

Customer-Facing Presto at Rippling – Andy Li, Rippling

Presto is used for a variety of cases, but tends to be used for larger scale analytical queries. We have been transitioning to using Presto to power our data platform and customer-facing scripting language, RQL (Rippling Query Language) to run arbitrary customer queries to power core products. Presto helps enable diverse, federated querying at scale. In this talk, Andy will cover where Presto sits in Rippling’s ecosystem as a core query layer, our collaboration and contributions for closer integration with Apache Pinot, and learnings on using Presto to handle a large variety of query patterns.

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.

Real Time Analytics at Uber with Presto-Pinot

In this talk, seasoned engineers at Uber will walk through the real time analytics use cases at Uber and the work they have done on the Presto architecture and the Presto-Pinot connector to address them.

Presto & the Foundations of Open Lake House: Trends & Opportunities – Biswapesh Chattopadhyay, Meta

Building open and shared foundational tech to build a lake house architecture can provide the best-of-breed user experience across the Analytics and ML domains and potentially beyond. In this talk, Biswa will share examples drawn from the evolution of the data stack at Meta over the last few years including efforts towards dialect unification (Sapphire aka Presto-on-Spark and Xstream-IE streaming engine efforts), eval unification (using Velox as the base), eliminating the need for data duplication for interactive analytics by building smart caching (RaptorX), building a best-of-breed file format that works across Analytics and ML (Alpha), and building an open source ML data pre-proc engine (TorchArrow) which shares the core dialect and eval components with Presto.

Open Source Data Lake Analytics: Trends and Opportunities – Biswapesh Chattopadhyay, Meta

Open source data analytics is undergoing an interesting transformation as the industry rapidly evolves around it. Accelerating migration to the cloud, the rise of immensely well funded proprietary vendors, fast evolving needs of the users all contribute to this. This talk goes into detail about the trends and opportunities in the OSS data analytics space, and a call to action on how this space can stay relevant.

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.

Panel: The Presto Ecosystem

The Presto Ecosystem – Moderated by Dipti Borkar, Ahana; Maxime Beauchemin, Preset; Vinoth Chandar, Apache Hudi; Kishore Gopalakrishna, Apache Pinot & James Sun, Facebook, Inc.