PrestoDB in HPE Ezmeral Unified Analytics – Milind Bhandarkar, HPE

PrestoDB in HPE Ezmeral Unified Analytics – Milind Bhandarkar, HPE


HPE Ezmeral Unified Analytics is an end-to-end data & AI/ML platform that consists of several popular open-source frameworks for data engineering, data analytics, data science, & ML engineering in a well-integrated packaging. These open-source frameworks include Apache Spark, Apache Airflow, Apache Superset, PrestoDB, MLFlow, Kubeflow, and Feast. This platform is built atop Kubernetes and provides built in security. In this talk we will focus on the role of PrestoDB in Unified Analytics as a fast SQL query engine, and also as a secure data access layer. We will discuss some of our value-additions to PrestoDB, such as a distributed memory-centric columnar caching layer that provides both explicit and transparent caching for dataset fragments, often leading to 3x to 4x query performance. We will conclude by proposing to make caching pluggable in PrestoDB and discussing future directions.

Extending Presto at LinkedIn with a Smart Catalog Layer LinkedIn

Extending Presto at LinkedIn with a Smart Catalog Layer LinkedIn

In this talk, Walaa describes how LinkedIn extended its Presto Hive Catalog with a smart logical abstraction layer that is capable of reasoning about logical views with UDFs by using two core components, Coral and Transport UDFs. Coral is a view virtualization library, powered by Apache Calcite, that represents views using their logical query plans. Walaa shows how LinkedIn leverages Coral abstractions to decouple view expression language from the execution engine, and hence execute non-Presto-SQL views inside Presto, and achieve on-the-fly query rewrite for data governance and query optimization.