In this talk, we will show you how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud.
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.
Tags: cloud bursting, hybrid cloud, multi cloud, remote data, spark, spark workloads
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.
Alluxio, an open source data orchestration technology, helping speed up Dataproc workloads by providing a distributed caching layer in the Dataproc Cluster.
Tags: cloud bursting, google dataproc, presto, spark, zero copy
The DBS team was tasked to solve their compute capacity problem. They wanted to provide faster insights and analyze data for a range of use cases but didn’t have the ability to scale compute elastically on-prem.
One use case that challenged them was customer call analysis. With the millions of customer calls they get every year, DBS manages over 50TB of customer data and audio files. This data needed to reside on-prem for compliance reasons. With on-prem compute limitations, they looked to the public cloud to analyze this data and selected “zero-copy” bursting as the best approach.
Tags: cloud bursting, hybrid bursting, zero copy
ODSC WEST 2019 Cloud storage brings great flexibility in management and cost-efficiency to data scientists, but also introduces new challenges related to data accessibility and data locality for machine learning applications. For instance, when the input data is stored in a remote cloud storage like AWS S3 or Azure blob storage, direct data access is … Continued
Tags: cloud bursting, compute storage separation, data orchestration, hdfs, hybrid cloud, machine learning, multi cloud, spark
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.
Tags: cloud bursting, hadoop, hybrid bursting, tech talk, zero copy