Alluxio on EMR: Fast Storage Access and Sharing for Spark Jobs

Traditionally, if you want to run a single Spark job on EMR, you might follow the steps: launching a cluster, running the job which reads data from storage layer like S3, performing transformations within RDD/Dataframe/Dataset, finally, sending the result back to S3. You end up having something like this.
If we add more Spark jobs across multiple clusters, you could have something like this.

Building a cloud-native analytics MPP database with Alluxio

This article walks through the journey of a startup HashData in Beijing to build a cloud-native high-performance MPP shared-everything architecture leveraging object storage as the data persistence layer and Alluxio as a data orchestration layer in the cloud.
we will illustrate how HDW leverages Alluxio as the data orchestration layer to eliminate the performance penalty introduced by object storage while benefiting from its scalability and cost-effectiveness.

Speeding Big Data Analytics on the Cloud with In-Memory Data Accelerator

Discontinuity in big data infrastructure drives storage disaggregation, especially in companies experiencing dramatic data growth after pivoting to AI and analytics. This data growth challenge makes disaggregating storage from compute attractive because the company can scale their storage capacity to match their data growth, independent of compute. This decoupled mode allows the separation of compute and storage, enabling users to rightsize hardware for each layer. Users can buy high-end CPU and memory configurations for the compute nodes, and storage nodes can be optimized for capacity.
This whitepaper is a continuation of Unlock Big Data Analytics Efficiency with Compute and Storage Disaggregation on Intel® Platforms

Distributed Data Querying with Alluxio

This is a guest blog by Jowanza Joseph with an original blog source. It is about how he used Alluxio to reduce p99 and p50 query latencies and optimized the overall platform costs for a distributed querying application. Jowanza walks through the product and architecture decisions that lead to our final architecture, discuss the tradeoffs, share some statistics on the improvements, and discuss future improvements to the system.

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.

Accelerate Spark and Hive Jobs on AWS S3 by 10x with Alluxio Tiered Storage

In this article, Thai Bui from Bazaarvoice describes how Bazaarvoice leverages Alluxio to build a tiered storage architecture with AWS S3 to maximize performance and minimize operating costs on running Big Data analytics on AWS EC2.
Takeaways: Common challenges in performance and cost to build an efficient big data analytics platform on AWS; How to setup Hive metastore to leverage Alluxio as the storage tier for “hot tables” backed by all tables on AWS S3 as the source of truth; How to setup tiered storage within Alluxio based on ZFS and NVMe on EC2 instances to maximize the read performance; Benchmark results of micro and real-world workloads.

Presto on Alluxio: How Netease Games leveraged Alluxio to boost ad hoc SQL on HDFS

Netease Games is the operator for many popular online games in China like “World of Warcraft” and “Hearthstone”. Netease Games also has developed quite a few popular games on its own such as “Fantasy Westward Journey 2”, “Westward Journey 2”, “World 3”, “League of Immortals”. The strong growth of the business drives the demand to build and maintain a data platform handling a massive amount of data and delivering insights promptly from the data. Given our data scale, it is very challenging to support high-performance ad-hoc queries to the data with results generated in a timely manner.

TalkingData Case Study: Leading Data Broker in China Leverages Alluxio to Unify Terabytes of Data Across Disparate Data Sources

TalkingData leverages Alluxio as a single platform to manage all the data across disparate data sources on-premise and in the cloud. Alluxio removes the complexity of our environment by abstracting the different data sources and providing a unified interface. Applications simply interact with Alluxio, and Alluxio manages data access to different storage systems on behalf of the applications. Alluxio effectively democratizes data access, allowing data scientists and analysts in various business units to accomplish their goals without needing to consider where the data is located or having to go to central IT or the engineering team to transfer or prepare the data.

Myntra Case Study: Accelerating Analytics in the Cloud for Customized Mobile E-Commerce

While looking for ways to streamline our data pipeline, we learned about Alluxio, an open source, memory speed, virtual distributed file system. We deployed Alluxio as the shared data layer for all of the intermediate stages in the data pipeline. By reading and writing data in Alluxio, the data can be read concurrently and stay in memory for the next stage of the pipeline. This increased the performance by speeding up the entire pipeline, and increased overall throughput of the pipeline allowing us to provide interactive response to our app users.