Join us for our 3rd Alluxio Day community virtual event featuring speakers from Nvidia, Alibaba, Aspect Analytics, and MSFT.
Tag: <span>tech talk</span>
Join us for this tech talk where we’ll introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform.
Google Cloud Dataproc is a widely used fully managed Spark and Hadoop service to run big data analytics and compute workloads in the cloud. Services like Dataproc reduce hardware spend, eliminate the need to overbuy capacity, and provide business agility. Yet users still face challenges for performance sensitive workloads or workloads running on remote data.
Alluxio is an open source cloud data orchestration platform that increases performance of analytic workloads running on Dataproc by intelligently caching data and bringing back lost data locality. Alluxio also enables users to run compute workloads against on-prem storage like Hadoop HDFS without any app changes.
Chris Crosbie and Roderick Yao from the Google Dataproc team and Dipti Borkar of Alluxio demo how to set up Google Cloud Dataproc with Alluxio so jobs can seamlessly read from and write to Cloud Storage. They also show how to run Dataproc Spark against a remote HDFS cluster.
If you’re a MapR user, you might have concerns with your existing data stack. Whether it’s the complexity of Hadoop, financial instability and no future MapR product roadmap, or no flexibility when it comes to co-locating storage and compute, MapR may no longer be working for you.
Alluxio can help you migrate to a modern, disaggregated data stack using any object store with the similar performance of Hadoop plus significant cost savings.
Join us for this tech talk where we’ll discuss how to separate your compute and storage on-prem and architect a new data stack that makes your object store the core. We’ll show you how to offload your MapR/HDFS compute to any object store and how to run all of your existing jobs as-is on Alluxio + object store.
In this tech talk, we’ll discuss why DBS turned to Alluxio’s bursting approach to help solve on-prem compute capacity challenges.
Want to leverage your existing investments in Hadoop with your data on-premise and still benefit from the elasticity of the cloud?
Like other Hadoop users, you most likely experience very large and busy Hadoop clusters, particularly when it comes to compute capacity. Bursting HDFS data to the cloud can bring challenges – network latency impacts performance, copying data via DistCP means maintaining duplicate data, and you may have to make application changes to accomodate the use of S3.
“Zero-copy” hybrid bursting with Alluxio keeps your data on-prem and syncs data to compute in the cloud so you can expand compute capacity, particularly for ephemeral Spark jobs.
This tech talk will share approaches to burst data to the cloud along with
how Alluxio can enable “zero-copy” bursting of Spark workloads to cloud data services like EMR and Dataproc. Learn how DBS bank uses Alluxio to solve for limited on-prem compute capacity.
EMR has become a widely used service to run big data analytics in the public cloud. But issues around slow/inconsistent EMR performance due to S3 data lakes creates challenges for organizations.
Alluxio is a data orchestration layer for the cloud that increases performance of analytic workloads running on AWS EMR using S3 as the storage.
Join us for this webinar where we will show you how to set up EMR Spark and Hive with Alluxio so jobs can seamlessly read from and write to your S3 data lake. You’ll see the performance gains with Alluxio in your EMR/S3 stack.
This tech talk gives shows how to set up EMR Spark and Hive with Alluxio to seamlessly read/write to your S3 data lake, along with performance benefits.