This article described how engineers at datasapiens brought down S3 API costs by 200x by implementing Alluxio as a data orchestration layer between S3 and Presto.
Tag: cloud object storage
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. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3, and others in public cloud, hybrid cloud, and multi-cloud environments.
Bay Area Meetup which include presentations on the architecture of Presto, its separation of compute and storage, cloud-readiness, recent advancements in the project such as Cost-Based Optimizer and Kubernetes Support. Presto and Alluxio production use cases and more.
Haoyuan Li’s keynote at O’Reilly Beijing discusses open source data orchestration and the value of leveraging Alluxio with rising trends driving the need for a new architecture. Four big trends driving this need: Separation of compute & storage, hybrid-multi cloud environments, rise of object store and self-service data across the enterprise.
Problem Sometimes big data analytics need process input data from two different storage systems at the same time. For instance, a data scientists may need to join two tables one from a HDFS cluster and one from S3. Existing Solutions Certain computation frameworks may be able to connect to storage systems including HDFS and popular cloud … Continued
A new generation of open source big data, represented by Alluxio, born at the University of California at Berkeley, looks at this issue. Different from systems such as designing storage tight coupling to achieve low-cost reliable storage HDFS, by providing a virtual data storage layer defined and implemented by software for data applications, abstracting and integrating cloudy, hybrid cloud, multi-data center and other environments The underlying files and objects, and through intelligent workload analysis and data management, make data close to computing and provide data locality, big data and machine learning applications can be achieved with the same performance and lower cost.
Enterprises are increasingly looking towards object stores to power their big data & machine learning workloads in a cost-effective way. The combination of SwiftStack and Alluxio together, enables users to seamlessly move towards a disaggregated architecture.
Alluxio can help data scientists and data engineers interact with different storage systems in a hybrid cloud environment. Using Alluxio as a data access layer for Big Data and Machine Learning applications, data processing pipelines can improve efficiency without explicit data ETL steps and the resulting data duplication across storage systems.
In this talk, we discuss how Alluxio can be deployed and used with a Spark data processing pipeline in the cloud. We show how pipeline stages can share data with Alluxio memory for improved performance benefits, and how Alluxio can improves completion times and reduces performance variability for Spark pipelines in the cloud.