A virtual data pipe is a collection of processes that extract raw data from different sources, transform it into an format that can be used by applications, and store it in a place like databases. This workflow can be configured to run in accordance with an interval or on demand. It is often complicated with a number of steps and dependencies. It should be easy to track the relationships between each step to ensure that everything is running smoothly.

Once the data has been taken in, a few initial cleaning and validating is performed. It could also be transformed using processes such as normalization and enrichment aggregation filtering as well as masking. This is an important step because it guarantees only the most accurate and reliable data is used virtual data pipeline for analytics.

Then, the data is consolidated and pushed into the final storage location where it can be easily accessed for analysis. It could be a data warehouse with a structure, such as an data warehouse, or a data lake that is less structured.

It is often desirable to adopt hybrid architectures, where data is transferred from on-premises to cloud storage. To accomplish this, IBM Virtual Data Pipeline (VDP) is a great choice as it provides an efficient multi-cloud copy management system that allows for testing and development environments to be separated 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.