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With the rise of Big Data platforms and cloud technologies, organizations know that their legacy EDWs are not the best means to serve their business interests. Whether it’s the high license and maintenance costs to third-party vendors, manual workflows that are stalled by clunky SDLC processes, or the inefficiency in consuming unstructured or semi-structured data for data science and advanced analytics, the legacy EDWs have to go.
While data migration can often be handled, where most organizations face bottlenecks in their migration path is the challenge in migrating their queries, models and schema that their legacy applications are founded on. “Lift and shift” approaches fail due to the confluence of performance and consumption issues that arise, so organizations must find a way to elegantly refactor and migrate their EDW to the modern platform, without impacting business consumption patterns.
At Next Pathway, our EDW migration service helps companies accelerate their migrations, and do so in a way to maintain governance of the data with minimal impact to routine business processes. Our methodology is founded on five basic steps:
- Inventory, Assessment, and Architecture
- Schema and Code Translation
- Optimization and Performance Tuning
- Data Migration
- Testing and Deployment
Our proprietary migration toolkit and accelerators help organizations automate large portions of their migration, while implementing architectures, operating models, and testing and deployment frameworks to accelerate the migration path, and help to decommission the legacy EDW in a timely, cost-efficient and governed manner.
Benefits of SHIFT™ versus manual migration:
“Our organization needed a solution to efficiently handle migrating high volume EDW workloads to the cloud. Past efforts with manual translation and development only saw our costs soar, and timelines further delayed.
By automating the code migration piece with Next Pathway’s SHIFT, we saw resourcing and operational expenses reduce by at least 50%.”
— a global retailer
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