A virtual data pipe is a collection of processes that take raw data from different sources, convert it into an format that can be used by applications, and save it to a destination like the database. This workflow can be configured according to a schedule or on demand. It can be complex with a number of steps and dependencies. It should be simple to monitor the connections between each process to make sure that it’s working properly.

Once the data has been taken in, a few initial cleaning and validation takes place. It may be transformed by processes such as normalization enrichment aggregation filtering, enrichment aggregation or masking. This is an important step since it ensures only the most accurate and reliable data is used for analytics.

Next, the data is collected and then moved to its final storage area where it is easily accessible for analysis. This could be a structured one such as a warehouse or a less structured data lake dependent on the requirements of the company.

It is generally recommended to adopt hybrid architectures, where data is transferred from storage on premises to cloud. IBM Virtual Data Pipeline is the ideal solution for this, since it offers an option for multi-cloud copies that allows application development and testing environments https://dataroomsystems.info/data-rooms-for-better-practice/ to be separated. VDP uses snapshots and changed-block tracking to capture application-consistent copies of data and provides them for developers through a self-service interface.