ALLUXIO COMMUNITY OFFICE HOUR
Accessing data to run analytic workloads in Spark across data centers and/or clouds can be challenging. Additionally, network I/O can bottleneck Spark jobs that need to read a large amount of data. A common solution is to deploy an HDFS cluster closer to Spark as a caching layer and manually copy the input data to HDFS first, purging it afterward. But this ETL process can be both time-consuming and also error-prone.
A more efficient and simpler solution is to run Spark on Alluxio as a distributed cache on top of the remote data source. While caching data transparently based on access patterns and storing the working set closer, Alluxio provides Spark jobs much higher I/O throughput with enhanced data locality. In addition, Alluxio also provides data accessibility and abstraction for deployments in hybrid and multi-cloud environments.
In this Office Hour, we will go over how to:
- Burst on-prem Spark workloads to the cloud with Alluxio so Spark can seamlessly read from and write to remote data storage
- Use Alluxio as the input/output for Spark applications
- Save and load Spark RDDs and Dataframes with Alluxio
ALLUXIO COMMUNITY OFFICE HOUR
Accessing data to run analytic workloads in Spark across data centers and/or clouds can be challenging. Additionally, network I/O can bottleneck Spark jobs that need to read a large amount of data. A common solution is to deploy an HDFS cluster closer to Spark as a caching layer and manually copy the input data to HDFS first, purging it afterward. But this ETL process can be both time-consuming and also error-prone.
A more efficient and simpler solution is to run Spark on Alluxio as a distributed cache on top of the remote data source. While caching data transparently based on access patterns and storing the working set closer, Alluxio provides Spark jobs much higher I/O throughput with enhanced data locality. In addition, Alluxio also provides data accessibility and abstraction for deployments in hybrid and multi-cloud environments.
In this Office Hour, we will go over how to:
- Burst on-prem Spark workloads to the cloud with Alluxio so Spark can seamlessly read from and write to remote data storage
- Use Alluxio as the input/output for Spark applications
- Save and load Spark RDDs and Dataframes with Alluxio
Videos:
Presentation Slides:
Complete the form below to access the full overview:
.png)
Videos
Pratik Mishra delivered insights on architecting scalable, deployable, and resilient AI infrastructure at scale. His discussion on fault tolerance, checkpoint optimization, and the democratization of AI compute through AMD's open ecosystem resonated strongly with the challenges teams face in production ML deployments.
In this talk, Bin Fan, VP of Technology at Alluxio, presents on building tiered architectures that bring sub-millisecond latency to S3-based workloads. The comparison showing Alluxio's 45x performance improvement over S3 Standard and 5x over S3 Express One Zone demonstrated the critical role the performance & caching layer plays in modern AI infrastructure.
In this talk, Greg Lindstrom shared how Blackout Power Trading achieved double-digit millisecond offline feature store performance using Alluxio, a game-changer for real-time power trading where every millisecond counts. The 60x latency reduction for inference queries was particularly impressive.