Manual migration is a high-risk approach to migrating data.

Manual migration is a custom, resource intense, and tactical approach to copying data. When administrators manually migrate data, they create, manage, schedule and maintain custom open-source scripts to migrate the large data sets. When a data transfer device is added to the big data to cloud migration plan, there is additional custom scripting required to upload the data.

The three big business risks with this manual approach to big data cloud migration are data inconsistency, business disruption, and high IT resource requirements. In each case, these business risks are avoidable utilizing Cirata’s Data Migrator.

RISK 1: Data inconsistency

Large data sets take time to bring to the cloud. 1 PB at 1 Gbps can take over 100 days to migrate. Even with a data transfer device, vendor load time takes weeks. While making data available in the cloud, change and ingest is still needed. Changing data during the lengthy migration time adds risk to bringing large scale data sets accurately to the cloud.

With manual migration relying on custom open-source scripts that focus on copying data, how does the team validate that the replication is accurate? Manual reconciliation at scale does not guarantee completely consistent data outcome. Also, how will administrators handle new updates that occurred during migration? Typically, data that are being modified or created during migration are not catered for with manual migration approaches.

There is a way to avoid the business risk of poor data quality. Cirata Dara Migrator is an automated approach to big data migration that provides validation of data consistency between the shared systems. As changes can occur anywhere in the system, Data Migrator ensures that the beneficiary has consistent data on completion. No data loss. No data quality uncertainty.

RISK 2: Business disruption

Organizations have invested increasingly mission-critical workloads to Hadoop because of scale and fit benefits. Enterprise-critical workloads bring with them expectations of availability, consistency, security, and auditability. On the spectrum of complexity, moving cold, static datasets is simple, while moving changing datasets with enterprise SLAs on these expectations is very challenging.

Manual migration often requires meaningful disruption of on-premises applications operations during big data migration. How much downtime is acceptable? Administrators who choose incremental migration strategies that bring data sets to the cloud over many months, face handling disruptive updates and incur the risk of not meeting their enterprise SLAs.

To avoid the risk of business disruption during migration, Data Migrator offers 100% business continuity for hybrid, multi-region and cloud environments with the continued operation of on-premises clusters. With no impact to donor cluster & operations during migration, this is the approach companies use to meet their critical SLAs.

RISK 3: High IT resources

The significant capital investments companies made to build out data centers to host their Hadoop data and workloads have just now moved past the typical 2 to 4-year depreciation period, allowing those costs to be written off. Shifting from capital hardware depreciation to operational expenditure for cloud becomes straightforward. Companies also have significant investments in people, processes, and applications supporting the on-premises data infrastructure.

Adding manual migration to these sunk costs is a risk to the IT budget. The overhead of activities to attempt non-disruptive, no-downtime big data migration are significant. What is the extent of resources required to create, test, manage, schedule and maintain custom migration scripts? Due to the custom nature of manual migrations, the program is prone to delays. For example, what resources are needed when transfers fail or are interrupted? What resources are needed to account for changes in the data during the migration?

With a proven, automated path to compelling cloud technologies, cost structures and analysis opportunities, leading companies are eliminating the risk of high cost of manual big data migration. Data Migrator offers the IT team automated migration at scale across all major commercial Hadoop distributions to cloud with a single scan of the source storage, even while data continues to change.

Solution: Cirata Data Migrator

Data Migrator is a fully automated solution that moves on-premise HDFS data, Hive metadata, local filesystem, or cloud data sources to any cloud or on-premises environment, even while those datasets are under active change. Data Migrator requires zero changes to applications or business operations. Moving data of any scale can begin immediately and be performed without production system downtime or business disruption, and with zero risk of data loss. Talk with an expert today to learn more!