Accelerating Analytical Workloads for Public & Hybrid Clouds

New York Meetup *

In this meetup, Dipti and HY will present a new approach to hybrid analytical workloads using Alluxio, an open source data orchestration layer, which sits between compute and storage layer. Applications like Apache Spark or TensorFlow can then seamlessly access multiple disparate data sources with consistent performance using data locality and abstraction that the data orchestration tier brings.

Recap: Presto Summit SF 2019

Alluxio is a proud sponsor and exhibitor at the Presto Summit in San Francisco.
What’s Presto Summit? It’s the leading Presto conference co-organized by our partner Starburst Data and the Presto Software Foundation.

O’Reilly AI Conference Keynote: Data Orchestration for AI, Big Data, and Cloud

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.

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Tech Talk: Accelerate Spark Workloads on S3

While running analytics workloads using EMR Spark on S3 is a common deployment today, many organizations face issues in performance and consistency. EMR can be bottlenecked when reading large amounts of data from S3, and sharing data across multiple stages of a pipeline can be difficult as S3 is eventually consistent for read-your-own-write scenarios.  

A simple solution is to run Spark on Alluxio as a distributed cache for S3. Alluxio stores data in memory close to Spark, providing high performance, in addition to providing data accessibility and abstraction for deployments in both public and hybrid clouds.

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Community Office Hour: Running Spark & Alluxio in Kubernetes

The data orchestration layer bridging the gap between data locality with improved performance and data accessibility for analytics workloads in Kubernetes, and enables portability across storage providers.
An overview of Alluxio and the cloud use case with Spark in Kubernetes. Learn how to set up Alluxio and Spark to run in Kubernetes.

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Building Fast SQL Analytics with Presto, Alluxio, and S3

Alluxio Community Office Hour *

Learn how to set up Presto with Alluxio such that Presto jobs can seamlessly read from and write to S3.
Compare the performance between Presto on S3 with Presto and Alluxio on S3.

Alluxio at Beijing Meetup

Haoyuan Li presents at Beijing Meetup on 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.

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Effective Data Engineering in the Cloud World

Cloud has changed the dynamics of data engineering as well as the behavior of data engineers in many ways. This is primarily because a data engineer on premise only dealt with databases and some parts of the hadoop stack.
In the cloud, things are a bit different. Data engineers suddenly need to think different and broader. Instead of being purely focused on data infrastructure, you are now almost a full stack engineer (leaving out the final end application perhaps). Compute, containers, storage, data movement, performance, network — skills are increasing needed across the broader stack. Here are some design concept and data stack elements to keep in mind.