The goal is to make Alluxio accessible to an even wider set of users through a focus on security, new language bindings, and further increased stability. In addition, the team is working on new APIs to allow applications to access data more efficiently and manage data across different under storage systems.
In this presentation, William Callaghan will focus on the challenges faced and lessons learned in building a human-in-the loop cyber threat analytics pipeline. They will discuss the topic of analytics in cybersecurity and highlight the use of technologies such as Spark Streaming/SQL, Cassandra, Kafka and Alluxio in creating an analytics architecture with missions-critical response times.
In this talk, we briefly introduce Alluxio, present several ways how Alluxio can help Spark be more effective, show benchmark results with Spark RDDs and DataFrames, and describe production deployments both Alluxio and Spark working together. In the meantime, we will provide live demos for some of the use cases.
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.
Alluxio, formerly Tachyon, is a memory speed virtual distributed storage system and leverages memory for storing data and accelerating access to data in different storage systems. Many organizations and deployments use Alluxio with Apache Spark, and some of them scale out to over PB’s of data. Alluxio can enable Spark to be even more effective, in both on-premise deployments and public cloud deployments. Alluxio bridges Spark applications with various storage systems and further accelerates data intensive applications. In this talk, we briefly introduce Alluxio, and present different ways how Alluxio can help Spark jobs. We discuss best practices of using Alluxio with Spark, including RDDs and DataFrames, as well as on-premise deployments and public cloud deployments.
Learn about stream processing on Alluxio from real-world workloads at Qunar, as well as how to position Alluxio in the streaming architecture. Xueyan Li and Yupeng Fu explore how Alluxio has led to performance improvements averaging a 300x improvement at service peak time on stream processing workloads at Qunar.
China Unicom is one of the five largest telecom operators in the world. China Unicom’s booming business in 4G and 5G networks has to serve an exploding base of hundreds of millions of smartphone users. This unprecedented growth brought enormous challenges and new requirements to the data processing infrastructure. The previous generation of its data processing system was based on IBM midrange computers, Oracle databases, and EMC storage devices. This architecture could not scale to process the amounts of data generated by the rapidly expanding number of mobile users. Even after deploying Hadoop and Greenplum database, it was still difficult to cover critical business scenarios with their varying massive data processing requirements. The complicated the architecture of its incumbent computing platform created a lot of new challenges to effectively use resources.
Haoyuan Li explores Alluxio’s goal of making its product accessible to an even wider set of users, through a focus on security, new language bindings, and further increased stability. Haoyuan also covers some new APIs Alluxio is working on to allow applications to access data more efficiently and manage data across different under storage systems.
Haoyuan Li and Cheng Chang explain how Alluxio makes Spark more effective in both on-premises and public cloud deployments and share production deployments of Alluxio and Spark working together. Along the way, they discuss best practices for using Alluxio with Spark, including with RDDs and DataFrames.