JD.com is one of the largest e-commerce corporations. In big data platform of JD.com, there are tens of thousands of nodes and tens of petabytes off-line data which require millions of spark and MapReduce jobs to process everyday. As the main query engine, thousands of machines work as Presto nodes and Presto plays an import role in the field of In-place analysis and BI tools. Meanwhile, Alluxio is deployed to improve the performance of Presto. The practice of Presto & Alluxio in JD.com benefits a lot of engineers and analysts.
Tag: <span>big data</span>
International Data Corporation (IDC) reported that the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 20251. This trend becomes more and more complicated with the variety and velocity of data growth, and it continuously changes the ways data is collected, stored, processed, and analyzed. New analytics solutions, including machine learning, deep learning, and artificial intelligence (AI), and new architectures and tools are being developed to extract and deliver value from the huge datasphere.
This article describes how Alluxio accelerates the training of deep learning models in a hybrid cloud environment with Intel’s Analytics Zoo open source platform, powered by oneAPI. Details on the new architecture and workflow, as well as Alluxio’s performance benefits and benchmarks results will be discussed.
Many organizations are leveraging EMR to run big data analytics on public cloud. However, reading and writing data to S3 directly can result in slow and inconsistent performance. Alluxio is a data orchestration layer for the cloud, and in this use case it caches data for S3, ensuring high and predictable performance as well as reduced network traffic.
JD.com is China’s largest online retailer. It uses Alluxio to provide support for ad hoc and real-time stream computing, using Alluxio-compatible HDFS URLs and Alluxio as a pluggable optimization component.
Running Spark with Alluxio is a popular stack particularly for hybrid environments. In this session, Dipti will briefly introduce Alluxio, share the top 10 tips for performance tuning for real-world workloads, and demo Alluxio with Spark.
Vitaliy and Dipti dive into how DBS Bank built a modern big data analytics stack, leveraging an object store as persistent storage even for data-intensive workloads, and how it uses Alluxio to orchestrate data locality and data access for Spark workloads.
In this talk, we present: trends and challenges in the data ecosystem in cloud era; Data engineering in the cloud with data orchestration; Use cases of using tech stacks (Presto or Tensorflow) with Alluxio on S3.
Haoyuan Li offers an overview of a data orchestration layer that provides a unified data access and caching layer for single cloud, hybrid, and multicloud deployments.