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.
Running PrestoDB on Kubernetes with Ahana Cloud and AWS EKS
PrestoDB is built to be cloud agnostic and container-friendly, but getting it to run on Kubernetes in the cloud can be challenging. In this talk, Gary Stafford (AWS) and Dipti Borkar (Ahana) will discuss: Why use the in-VPC deployment model with AWS and demo, etc – Deploying PrestoDB on AWS EKS using the Ahana Cloud managed service within the user’s AWS account.
Presto on Spark – Facebook – Virtual Meetup
At Facebook, we have spent the past several years in independently building and scaling both Presto and Spark to Facebook scale batch workloads. It is now increasingly evident that there is significant value in coupling Presto’s state-of-art low-latency evaluation with Spark’s robust and fault tolerant execution engine. In this talk, we’ll take a deep dive in Presto and Spark architecture with a focus on key differentiators (e.g., disaggregated shuffle) that are required to further scale Presto.
Building a Modern Data Platform with Presto – Denis Krivenko, Platform24
Hadoop era is gone. Cloud computing is today’s reality. But… What if you cannot use public clouds? What if your cloud does not provide data platform capabilities? What if you want your solution to be cloud agnostic? In this case you create your own cloud native data platform on Kubernetes. In the session Denis will talk about reasons for building analytics data platform solution in Platform24, cloud native data platform architecture principles, data stack they use and why Presto plays one of the key roles in it.
Presto On Spark: Scaling not Failing with Spark – Ariel Weisberg, Meta & Shradha Ambekar, Intuit
Presto on Spark is an integration between Presto and Spark that leverages Presto’s compiler/evaluation as a library and Spark’s large scale processing capabilities. It enables a unified SQL experience between interactive and batch use cases. A unified option for batch data processing and ad hoc is very important for creating the experience of queries that scale instead of fail without requiring rewrites between different SQL dialects. In this session, we’ll talk about Presto On Spark architecture, why it matters and its implementation/usage at Intuit.