What is a Virtual Data Pipeline?

What is a Virtual Data Pipeline?

A virtual data pipeline is a collection of processes that take raw data from source systems and converts it into a format that is able for use by applications. Pipelines can serve many purposes, including analytics, reporting and machine learning. They can be configured to run data according to a schedule or on demand. They can also be utilized for real-time processing.

Data pipelines are often complex, with numerous steps and dependencies. Data generated by a single application can be transferred to multiple pipelines that feed additional applications. It is essential to be able to monitor these processes and their relationships to ensure that the pipeline is operating properly.

Data pipelines are used in three different ways: to speed development, improve business intelligence, as well as reduce risk. In each of these cases, it is the goal to gather a lot of data and convert it into a format that can be used.

A typical data pipeline would comprise several transformations, such as reduction, filtering and aggregation. Each stage of transformation may require an additional data store. After all transformations are completed the data is then pushed into the destination database.

Virtualization is a method of reducing the time needed to capture 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.

With IBM Cloud Pak for Data powered by Actifio you can easily deploy a virtual data pipeline to enhance DevOps capabilities and speed up cloud data analytics and AI/ML initiatives. The patented virtual data pipe solution from IBM provides an efficient multi-cloud copy management system that decouples test and development infrastructure from production environments. IT administrators can quickly allow testing and development by supplying masked copies on-premises databases through a self-service GUI.


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