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

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.

Building Modern Data Lakes for Analytics Using Object Storage – Satish Ramakrishnan, MinIO

Building Modern Data Lakes for Analytics Using Object Storage – Satish Ramakrishnan, MinIO

The modern data lake is distributed, unstructured and demands performance and scale – or better stated, performance at scale. Modern object stores are the ideal platform to pair with MPP query engines like Presto – particularly as the scale reaches tens or hundreds of petabytes with tens to hundreds of concurrent queries. In this talk, Satish Ramakrishnan will outline the better together attributes of the two technologies with a focus on the most sophisticated modern object storage features – from throughput optimizations, multi-cloud capabilities, cross-cloud active active replication and lifecycle management. Participants will come away with a reference architecture suited to query processing at object scale.

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

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.