Cloud Migration

Fast analytical performance is crucial for effective data analysis, data-informed decision making, and business competitiveness.

When long-running and complex queries run on the same on -premises machine, compute and storage are tightly coupled without separation. In this structure, either compute suffers in favor of storage prioritization, or vice versa. This resource-sharing can create scalability and speed issues.

Every organization wants to scale with systems that can keep up with growth. While legacy databases are capable of scaling, doing so typically requires bringing down the server and spinning up a new one over the course of roughly an hour—which simultaneously puts business on hold. Disruptions equal timeouts on the client side, lost income, and frustrations. 

Data sharing ensures business teams have broader access to the most relevant and near real time data necessary to accelerate business growth. In a legacy system, it is extremely common for databases to build additional logic into the data pipeline that allows users to copy data sets across environments. 

In most legacy databases, users can stop or pause servers when not in use, but there is no sufficient solution to monitor and detect user demand for unique servers. As a result, many businesses run them around the clock and pay the associated price. If they pause a server, businesses are billed hourly even if the server only runs for a few minutes. 

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