Videos Archive - PrestoDB

Presto at Adobe: How Adobe Advertising uses Presto for Adhoc Query, Custom Reporting, and Internal Pipelines

Rajmani Arya, Varun Senthilnathan & Manoj Kumar Dhakad, Adobe Advertising: We are from the Product Engineering team in Adobe Advertising (https://business.adobe.com/in/product…. Adobe Advertising is a digital advertisement platform. We take care of accumulating all data, providing platform intelligence, building and maintaining machine leaning capabilities, building and maintaining internal pipelines that form derived data to be used by other teams. The volume of total incoming raw data ranges between 8 to 10 tb/ day spread across 7 regions. The total data in the system currently is about 7pb. This data is largely stored in Hive tables with a central metastore. We use Presto in three ways: 1. Data studio – an internal tool to enable data analysts, sales, marketing and other teams to do adhoc querying. This is also used by data engineers to do adhoc querying for engineering tasks. 2. Custom Reports – We create reports for customers to get performance insights on their campaigns. We have 100s of reports that are run on a daily basis. 3. Internal Pipelines – Presto is used to retrieve data to power 100s of pipelines run daily to generate derived data.

Ending DAG Distress: Building Self-Orchestrating Pipelines for Presto – Roy Hasson, Upsolver

Ending DAG Distress: Building Self-Orchestrating Pipelines for Presto – Roy Hasson, Upsolver dbt and Airflow is a popular combination for creating and scheduling batch data modeling and transformation jobs that execute in a data warehouse like Snowflake. Presto users querying the data lake need a similar solution that is simple to use and makes it easy to ingest, model, transform and maintain datasets, without having to write or manage complex DAGs. In this session you will learn how Upsolver built a tool that allows engineers, developers and analysts to write data pipelines using SQL. Pipelines are automatically orchestrated, are data-aware and maintain a consistent data contract between each stage of the pipeline. You will also learn how to introduce the idea of data products into your company to enable more self-service for your Presto users.

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.

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.

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.