Accelerating Hive with Alluxio on S3

Alluxio Community Office Hour *

Hear about Bazaarvoice’s use case leveraging Apache Spark, Hive, and Alluxio on S3. And learn how to set up Hive with Alluxio so that Hive jobs can seamlessly read/write to S3.

Accelerating Write-intensive Data Workloads on AWS S3

Alluxio is an open-source data orchestration system widely used to speed up data-intensive workloads in the cloud. Alluxio v2.0 introduced Replicated Async Write to allow users to complete writes to Alluxio file system and return quickly with high application performance, while still providing users with peace of mind that data will be persisted to the chosen under storage like S3 in the background.

Efficient Data Engineering with Apache Spark, Hive, and Alluxio on S3

Alluxio Meetup | Austin *

Welcome to the first event of the Cloud, Data, & Orchestration Austin Meetup! This meetup will feature two talks and an opportunity to engage with other data engineers, developers, and Alluxio users. Thanks to Bazaarvoice for hosting!

Hybrid Cloud Analytics: Scaling analytics workloads on on-premise to public clouds with Alluxio

This whitepaper details how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud. We will show an example of what it might look like to run on-demand Starburst Presto, Spark, and Hive with Alluxio in the public cloud using on-prem HDFS.

The paper also includes a real world case study on a leading hedge fund based in New York City, who deployed large clusters of Google Compute Engine VMs with Spark and Alluxio using on-prem HDFS as the underlying storage tier.

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How do you modify location metadata in Hive?

Problem If you have hundreds of external tables defined in Hive, what is the easist way to change those references to point to new locations? That is a fairly normal challenge for those that want to integrate Alluxio into their stack. A typical setup that we will see is that users will have Spark-SQL or … Continued

How do you partition Hive Table across storage systems using Alluxio?

Today when we create a Hive table, it is a common technique to partition the table across different values and ranges to improve query performance and reduce maintenance cost. However, Hive can not  access a single table directly using a single query with the data of this Hive table across different mediums of storage and … Continued

China Unicom Uses Alluxio and Spark to Build New Computing Platform to Serve Mobile Users

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.

How to Use Alluxio to improve Spark and Hadoop HDFS Performance of Data Access and System Reliability [Chinese]

Database Technology Conference China 2017 *

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.