A virtual data pipe is a set of processes that take raw data from different sources, transform it into the format that can be utilized by software, and store it in a place like a database. The workflow can be programmed to run in accordance with an interval or on demand. As such, it is often complex with many steps and dependencies. Ideally it should be easy to monitor each process and its connections to ensure that all processes are operating correctly.
After the data is consumed, some initial cleaning and validation takes place. It may also be transformed through processes such as normalization or enrichment aggregation filters, or masking. This can be an important step to ensure that only the most reliable and accurate data is utilized for analytics and application use.
The data is then consolidated before being moved to its final storage place in order to be accessed to analyze. It could be a database that has a structure, such as the data warehouse, or a data lake that is not as structured.
It is generally recommended to implement hybrid architectures in which data is transferred from storage on premises to cloud. IBM Virtual data rooms Data Pipeline is an excellent option to accomplish 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.