A virtual data pipe is a collection of processes that gather raw data from different sources, transform it into an format that can be used by software, and store it in a location like databases. The workflow can be configured according to a schedule or as needed. As such, it is often complex with many steps and dependencies – ideally it should be simple to track each step and its interrelations to ensure that all operations are running smoothly.
Once the data has been ingested, it goes through a process of initial cleaning and validation. It can be transformed by processes such as normalization, enrichment, aggregation, filtering or masking. This is a crucial step to ensure that only the most accurate and reliable data is used visit their website https://dataroomsystems.info/data-rooms-for-better-practice/ for analytics and application use.
The data is then consolidated, and moved to the final storage location in order to be accessed to analyze. It could be a structured database, such as a warehouse or less structured data lake, dependent on the requirements of an organization.
To accelerate deployment and enhance business intelligence, it’s usually desirable to use an hybrid architecture in which data is transferred between on-premises and cloud storage. To achieve this, IBM Virtual Data Pipeline (VDP) is an excellent choice since it offers an efficient multi-cloud copy management solution that enables testing and development environments 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.