White Papers

On-premises to Cloud Migration

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White Paper Data-driven enterprises are incorporating the power of data processing, analytics, and ML frameworks like Presto, Airflow, Apache Spark, RStudio and others for their ad-hoc analytics, streaming analytics and ML workloads. But they are also discovering some of the challenges of operating these technologies in on-premises data lake environments. On-premise Data Lake environments have a fixed amount of computing resources. Cloud based data lakes provide the best ROI when underlying compute resources are elastic and enable auto scaling up and down depending on the type, volume and SLAs of workloads. Although cloud data lakes solve data migration challenges with quick to start and easy to use storage migration services, job and workload migration challenges still remain there. Teams still need to put additional effort into provisioning resources and handling uneven workload demand at large scale in real-time for creating data pipelines. Therefore teams need data lake platforms for workload and job migration without disrupting their data lake users. Following reasons should be kept at the forefront as the migration is done. Top Reasons for Migrating Data Lake Workloads from On-premises to Cloud Dhiraj Sehgal, Vihag Gupta, Ashish Kumar

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