In the on-prem days, one key performance optimization for Apache Hadoop or Apache Spark workloads is to run tasks on nodes with local HDFS data. However, while adoption of the Cloud & Kubernetes makes scaling compute workloads exceptionally easy, HDFS is often not an option. Effectively accessing data from cloud-native storage services like AWS S3 or even on-premises HDFS becomes harder as data locality is lost.
Join us for this tech talk where we’ll introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform.
The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. While this enables scaling elasticity, it introduces new problems – how do you co-locate data with compute, how do you unify data across multiple remote clouds, how do you keep storage and I/O service costs down and many more.
In this office hour, we will go over an introduction and motivation of Alluxio Structured Data Management, an overview of the different services in Alluxio 2.1, and a demo using Alluxio Structured Data Management with Presto.
This talk describes a stack of open-source projects to serve high-concurrent and low-latency SQL queries using Presto with Alluxio on big data in the cloud. Deploying Alluxio as a data orchestration layer to access cloud storage object storage (e.g., AWS S3), this architecture greatly enhances the data locality of Presto with distributed and cross-query caching, thus avoids reading the same data repeatedly from the cloud storage.
The Presto Summit continues to bring together the best developers, engineers, data scientists, and executives from the Presto community to share how some of the largest and most innovative companies are using this technology to power their analytics platforms.
Alluxio, an open source data orchestration technology, helping speed up Dataproc workloads by providing a distributed caching layer in the Dataproc Cluster.
This talk describes a stack of open-source projects to serve high-concurrent and low-latency SQL queries using Presto with Alluxio on big data in the cloud. Deploying Alluxio as a data orchestration layer to access cloud storage object storage (e.g., AWS S3), this architecture greatly enhances the data locality of Presto with distributed and cross-query caching, thus avoids reading same data repeatedly from the cloud storage.
Presto is widely used for data science, business analytics, and operations. Presto’s SQL is a main driver for this, as it is ANSI-compliant, easy to ramp-up, and has rich functionality. Given the versatility and flexibility of this software, there is also a huge demand to develop interfaces for other critical data domains like real-time dashboards, stream processing, and large-scale batch computations. We will explore some interesting systems and prototypes to bring Presto to these new domains.