Just a few years ago, the idea of using the cloud to run your data and analytics workloads might have seemed odd. But with cloud migration driven at the highest levels, including corporate boardrooms in the private sector and government mandates such as the Federal Data Strategy in the public sector, this perception has changed dramatically.
Today, analytics, data warehouses, data lakes, data science projects, graph analysis, and streaming analytics are migrating to the cloud at an astonishing pace. A recent survey of five hundred enterprises by Intelligent Business Strategies demonstrates the extent of this trend. Here are just a few of the survey findings:
- 51.8% of companies are migrating analytical workloads to the cloud today
- 42.4% have data warehouse migrations underway
- 35.8% said that they expected to migrate as much as 60% of their analytical workloads by 2022
Big Efforts Deserve the Most Powerful Tools
As anyone who has ever worked on a system migration can tell you, it is a big effort with lots of moving parts before, during, and after the migration. Rarely a straightforward, one-for-one lift and shift, myriad transformations are typically required. Furthermore, as these are typically critical, run-the-business systems, there is never a window to just shut things down, nor tolerance, if for some reason, the migration fails.
When multiplied by the number of systems targeted for migration at a typical enterprise, these efforts along with their associated risk, becomes absolutely huge. So huge that it is worth investing in powerful tools that can make these migrations easier and faster.
Data Virtualization is that powerful tool.
Every Tool Needs a Manual
Like many tools in your IT toolbox, data virtualization is extremely flexible, helping solve a wide variety of data challenges from project-level data federation to higher-order data architectures such as virtual data layer, logical data warehouse, and data-as-a-service.Data virtualization is extremely flexible, helping solve a wide variety of data challenges from project-level data federation to higher-order data architectures such as virtual data layer, logical data warehouse, and data-as-a-service. Click To Tweet
So how does data virtualization help speed your cloud migration? In his whitepaper, “Accelerating Workload Migration to the Cloud Using Data Virtualisation,” Mike Ferguson of Intelligent Business Strategies provides the answer. This paper is like a how-to manual, guiding you from start to finish, including:
- The data challenges organizations face when migrating to the cloud
- Key requirements and activities before, during, and after migration
- How to use data virtualization to accelerate both operational and analytical system migrations
- How you can benefit from ongoing use of data virtualization in a hybrid, multi-cloud environment.
For more on how data virtualization can help you migrate to the cloud faster, watch this webinar on top challenges and “must-dos” for ensuring a smooth, rapid migration.