A virtual data pipe is a set of processes that take raw data from various sources, transform it into a format that can be utilized by programs, and store it in a place like databases. This workflow is able to be set according to a set schedule or as needed. It is usually complex, with many steps and dependencies. It should be easy to keep track of the relationships between each process to make sure that everything is running smoothly.
Once the data has been consumed, some initial cleaning and validating takes place. It may also be transformed using processes like normalization enrichment aggregation filtering, enrichment aggregation or masking. This can be an important step to ensure that only the most reliable and accurate data is used for analysis and application use.
The data is then consolidated, and moved to the final storage location which can then be accessed to analyze. It could be a database that has an organized structure, like an data warehouse, or a data lake which is less structured.
It link is generally recommended to adopt hybrid architectures, where data is transferred from on-premises to cloud storage. IBM Virtual Data Pipeline is an excellent choice to achieve this, as it offers a multi-cloud copy solution that allows development and testing environments 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.