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A virtual data pipeline is a collection of processes that take raw data from different sources, converts it into a format that is usable to be used by applications and then saves it in a destination system, such as a database or data lake. This workflow can be set to run in accordance with the timetable or at any point. It is often complex and has many steps and dependencies. It should be easy to monitor the connections between each step to ensure that it’s running as planned.

Once the data has been taken in, a preliminary cleaning and validating is performed. It can be transformed by using processes like normalization or enrichment aggregation filters, or masking. This is an essential step to ensure that only the most accurate and reliable data is used for analysis and application use.

The data is then consolidated, and moved to its final storage location which can then be accessible for analysis. It could be a data warehouse with an organized structure, like the data warehouse, or a data lake that is less structured.

To speed up deployment and improve business intelligence, it’s often recommended to implement an hybrid architecture in which data is moved between cloud and on-premises storage. To achieve this, IBM Virtual Data Pipeline (VDP) is a great choice as it provides an efficient, multi-cloud copy management solution that allows applications development and test environments to be separated from production infrastructure. VDP uses snapshots and changed-block tracking to capture application-consistent copies of data and provides them for developers through a self-service interface.