A virtual data pipeline is a set of processes that takes raw data from source systems and converts it into a format that is able for consumption by applications. Pipelines are beneficial for a variety of reasons, including analytics, reporting, and machine learning. They can be configured to process data on an established schedule or on demand, and may be used for real-time processing.
Data pipelines are typically complex, involving many steps and dependencies. Data generated by a single application can be sent to multiple pipelines, which feed additional applications. It is crucial to keep track of these processes as well as their relationships to ensure that the pipeline is operating correctly.
Data pipelines can be used in three main ways: to speed up development, enhance business intelligence, and lower risk. In each of these cases it is the intention to take a large amount of data and then transform it into a form that can be utilized.
A typical data pipeline consists of various transformations, such as filtering and aggregation. Each stage of transformation will require a different type of data store. When all the transformations are completed the data is then pushed into the destination database.
To reduce the time required to capture and transport data Virtualization technology is often used. 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 set up a virtual data pipe quickly and easily. This will facilitate DevOps and accelerate cloud data analysis as well as AI/ML initiatives. The patented virtual data pipeline solution by IBM provides a powerful multi-cloud copy management platform that decouples test and development infrastructure from production environments. IT administrators can quickly allow test and development by setting up the databases with masked copies using the self-service GUI.