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What is a Virtual Data Pipeline?

A virtual data pipe is a collection of processes that transform raw data gathered from source systems into an format that can be accessed by applications. Pipelines can be used for a variety of reasons, including analytics, reporting, and machine learning. They can be programmed to run data according to a schedule or on demand. They can also be used for real-time processing.

Data pipelines are often complex with many steps and dependencies. Data generated by one application can be fed to multiple pipelines which feed additional applications. The ability to track these processes, and their relationships to one another is essential to ensure that the entire pipeline functions properly.

There are three primary uses scenarios for data pipelines: speeding up development, enhancing business intelligence and mitigating risk. In each case, it is the goal to take a large amount of data and turn it into a format that can be used.

A typical data pipeline would comprise various transformations like reduction, filtering and aggregation. Each stage of transformation can require a different type of data store. Once all of the transformations have been completed the data will be pushed into the destination database.

Virtualization can be used to cut down the time needed to collect and transfer data. This allows the use of snapshots and changed-block tracking to capture application-consistent copies of data in a much faster way than traditional methods.

IBM Cloud Pak for Data powered by Actifio permits you to deploy a virtual data pipe quickly and easily. This will enable DevOps and accelerate cloud data analysis and AI/ML initiatives. The patented virtual pipe solution by IBM provides an efficient multi-cloud copy control platform that separates development and test infrastructure from production environments. IT administrators can provision anonymized copies of databases on premises via a self-service interface to facilitate development and testing.

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