Automated Workload Transformation to Cloud: The Future-proof Approach
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Enterprises have been dependent on traditional data warehouses to ingest, model, and store data for ages. In a typical IT environment, conventional data warehouses would deploy, extract, transform, and load (ETL) jobs to process data in batches. However, handling millions of queries per month come at a considerable cost. Blending petabytes and exabytes of data from various historical and streaming sources such as internal data across spreadsheets, third-party data, and big data stores also make business analysis difficult and time-consuming.
As businesses explore options to shift from traditional data warehouses to meet their demands and scale business operations, the open stack-based cloud platform has gained popularity. Whether it is public, private, or hybrid, enterprises are continuing to move their workloads and applications to the cloud infrastructure. Gartner predicts that more than 50 percent organizations using the cloud today will have all their workloads in the cloud by 2021.
Enterprises will no longer lift-and-shift into the cloud but will instead refactor and rebuild directly in the cloud. While enterprises might retain some mission-critical workloads on-premise, most enterprise data will be in the cloud.
In this white paper, we will explore:
- Reasons to move to the cloud
- Challenges of cloud adoption
- Steps to move to the cloud
- How to manage the transformation
Download the white paper to learn more.