Transitioning from Legacy Systems

Always involves Data migration and can follow one of 3 paths

1. Lift and Shift:

Moving existing workloads to cloud infrastructure as a service―leveraging only the compute, storage and network capabilities of the cloud―eliminates the need for complex application rewrites while offering the benefit of scalable infrastructure.

2. Decommissioning Legacy Data Over Time:

You keep your existing data on your legacy systems and send new data directly to the new, cloud-based platform—with no data migration. New features and functionality are designed to be cloud-ready.

3. Complex Data Transformations:

This involves a complete modernization of data-driven applications and is most applicable when applications are nearing end of life. Examples include transitioning from mainframe, AS/400 and older relational database management systems to new databases such as Hive, Hadoop and HBase.

Skills

Big data implementations depend on diverse skills, including those of developers, administrators and cloud and big data architects. Demand for such experts exceeds supply, so companies often ask internal or contract personnel to work beyond their core competencies, which can slow implementations. It is much more economical to choose a vendor that provides these capabilities on a turnkey basis. Be sure it has managed multiple, complex big data environments at scale on dedicated environments and public clouds.