A virtual data pipeline is a set or processes which extract raw data from a variety of sources, converts it into a format that is usable for use by applications, and then stores it in a storage system, such as a database or data lake. This workflow can be configured according to a predetermined schedule or on demand. This is often complex, with lots of steps and dependencies. Ideally it should be simple to monitor each process and its relationships to ensure that all processes are operating correctly.
After the data is taken in, a few initial cleaning and validating is performed. It could be transformed by using processes like normalization, enrichment aggregation filtering or masking. This is a crucial step, since it guarantees that only the most reliable and accurate data is used for analytics and application usage.
The data is then consolidated and moved to its final storage location which can then be used for analysis. It could be a data warehouse with an organized structure, like a data warehouse or a data lake that is less structured.
To speed up deployment and improve business intelligence, it’s often beneficial to utilize a hybrid architecture where data is moved between cloud storage and on-premises. To do this effectively, IBM Virtual Data Pipeline (VDP) is a fantastic choice as it provides an efficient multi-cloud copy management solution that permits applications development and test environments to be decoupled 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.