Simplified Data Preparation for Machine Learning in Hybrid and Multi Clouds

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

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Tech Talk: From limited Hadoop compute capacity to increased data scientist efficiency

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. 

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