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Request DemoFacing budget constraints for new infrastructure upgrades? This method enables you to upgrade your cluster while conserving resources within a planned maintenance window.
No additional infrastructure investment required.
No data movement concerns.
Parallel installation alongside existing Hadoop binaries.
Step-by-step guidance provided by Runbook(s).
Plan for an 8-hour downtime window.
Allocate 4 hours for rollback procedures.
Ensure job alignment or modification ahead of time if reliant on legacy jars.
Upgrade smoothly with minimal maintenance and ample time to validate ODP's performance.
Minimal upgrade downtime (< 1hr).
Rapid rollback capability (< 1hr).
Gradual job deployment for streamlined implementation.
Ample bandwidth for benchmarking and application validation.
Plan for parallel hosting or infrastructure costs until cutover.
Prepare for data movement or forking ingestion pipelines to the new cluster.
Establish a parallel ODP cluster either on-premises or in the cloud and execute the migration.
Minimal upgrade downtime required (< 1hr).
Quick rollback capability (< 1hr).
One-time data and job movement could be performed in phases as well.
Ideal for hardware refresh/ infra change motions.
Allocate resources for running a similarly sized cluster.
Prepare for data ingestion pipeline forking or data copying from the old to the new cluster.
PhonePe uses Acceldata to scale open-source data platform by 10x
Break free from vendor lock-in and access a fully open-source platform for modern data-driven enterprises.
Request DemoBreak free from vendor lock-in and access a fully open-source platform for modern data-driven enterprises.
Book a DemoODP (Open Data Platform) is an innovative "Interoperable Data & AI Platform" from Acceldata, empowering data-driven enterprises. It allows data leaders to leverage the Apache Hadoop ecosystem and open-source tools while avoiding vendor lock-in and proprietary traps. With access to a global community, rapid security resolutions, active open-source participation, and expert support, ODP offers the flexibility and growth essential for modern enterprises.
Get this guide for insights on Hadoop ecosystem challenges, solutions, success stories, and more.
Get Free GuideFacing budget constraints for new infrastructure upgrades? This method enables you to upgrade your cluster while conserving resources within a planned maintenance window.
No additional infrastructure investment required.
No data movement concerns.
Parallel installation alongside existing Hadoop binaries.
Step-by-step guidance provided by Runbook(s).
Plan for an 8-hour downtime window.
Allocate 4 hours for rollback procedures.
Ensure job alignment or modification ahead of time if reliant on legacy jars.
Upgrade smoothly with minimal maintenance and ample time to validate ODP's performance.
Minimal upgrade downtime (< 1hr).
Rapid rollback capability (< 1hr).
Gradual job deployment for streamlined implementation.
Ample bandwidth for benchmarking and application validation.
Plan for parallel hosting or infrastructure costs until cutover.
Prepare for data movement or forking ingestion pipelines to the new cluster.
Establish a parallel ODP cluster either on-premises or in the cloud and execute the migration.
Minimal upgrade downtime required (< 1hr).
Quick rollback capability (< 1hr).
One-time data and job movement could be performed in phases as well.
Ideal for hardware refresh/ infra change motions.
Allocate resources for running a similarly sized cluster.
Prepare for data ingestion pipeline forking or data copying from the old to the new cluster.
Enhance performance, scalability, and reliability, all while cutting costs.
Explore PulseCompute : EC2, EKS
Storage : Disk Mounts, S3
Backend DB : RDS (MySQL/Postgres)
Compute : Virtual Machine (VM), AKS
Storage : ADLS v2, Disk Mounts
Backend DB : Azure SQL (MySQL/Postgres)
Compute : Virtual Machine (VM), GKE
Storage : Disk Mounts, GCS
Backend DB : Cloud SQL (MySQL/Postgres)
Compute : Virtual Machine (VM), Bare Metal (BM) Servers, Kubernetes
Storage : Disk Mounts (JBOD, RAID, SAN), Dell Isilon, S3, ADLS V2, GCS
Backend DB : Postgres, MySQL, Oracle