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.

Presto on Kafka at Scale – Yang Yang & Yupeng Fu, Uber

Presto on Kafka at Scale – Yang Yang & Yupeng Fu, Uber

Presto is a popular distributed SQL query engine for running interactive analytic queries. Presto provides a Connector API that allows plugins to dozens of data sources, and thus positions itself as a single point of access to a wide variety of data. At Uber, we significantly improved Presto’s Kafka connector to meet Uber’s scale. For example, the new connector allows dynamic Kafka cluster and topic discovery so users can directly query existing Kafka topics without any registration and onboarding process; dynamic schema discovery allows fetching the latest schema without any Presto restart or deployment; smart time range suggestions to users based on Kafka metadata analysis to avoid large-range scans and thus keep the query interactive.