Enhance Cloud Migration

Simplify and speed cloud migration with Molecula

The value proposition of cloud computing extends from cost savings to flexible scalability to enhanced security. Resources can be provisioned on an as-needed basis, and paying only for what you use can result in significant cost savings. The ability to access data from anywhere is also an obvious advantage to cloud computing. However, there are big data problems that the cloud won’t solve, and in some cases will exacerbate. Some professionals are surprised when they find pay-per-use models can lead to unpredictable costs. 

In Flexera’s 2020 State of the Cloud report, the top cloud initiative according to respondents is, “optimizing existing use of cloud to save costs,” (73%) for the fourth year in a row. To make the most of cloud computing benefits, properly preparing and reducing unnecessary data is key.  Implementing a system that does not rely on copying or federation will provide greater cost savings in resources both on premises and in the cloud.

Some of the challenges facing companies managing cloud migration today include: Understanding app dependencies and providing proper data access Assessing on-premises vs. cloud costs Right sizing/selecting best instance Migrating the right apps and data to the cloud Achieving machine scale analytics across distributed data

Molecula with Cloud Migration 

Organizations who migrate data to the cloud will successfully simplify their data infrastructure and reduce resource costs however cloud migration does not provide the ability to do machine scale analytics. Molecula’s enterprise feature store compliments cloud migration by enabling  machine scale analytics and AI across data in the cloud and distributed in other places. With Molecula, companies can choose the right data to move to the cloud and have the option to leave other data at its source. Because a feature-based data format enables millisecond query performance and has a 60-90% reduced footprint compared to original data, organizations achieve real-time use cases never possible before and reduce the overall costs of enabling machine scale analytics and AI.