A virtual data pipeline is a set of procedures that extracts raw data from a variety of sources, transforms it into a usable format for use by applications, and then store it in a destination system such as a database or data lake. The workflow can be programmed to run in accordance with a timetable or on demand. As such, it is usually complex, with many steps and dependencies – ideally it should be able to monitor each process and its interrelations to ensure that all operations are operating correctly.

After the data is ingested, it goes through a process of initial cleansing and validation. It can be transformed at this stage through processes like normalization enrichment, aggregation, filtering or masking. This is an important process because it ensures that only the most precise and reliable data can be utilized for analytics.

The data is then consolidated and moved to its final storage spot, where it can easily be access for analysis. It may be a database with an organized structure, like an data warehouse, or a data lake that is not as structured.

It is usually recommended to utilize hybrid architectures where data is transferred from storage on premises to cloud. To do this effectively, IBM Virtual Data Pipeline (VDP) is a great choice because it is an efficient, multi-cloud copy management solution that allows for the development https://dataroomsystems.info/data-rooms-for-better-practice/ and testing environments of applications to be separated from the 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.