A virtual data pipeline is a collection of processes that take raw data from sources and converts it into a format that is able to be accessed by applications. Pipelines can serve a variety of purposes, including reports, analytics, and machine learning. They can be configured to run data on a schedule or at any time. They can also be utilized for real-time processing.
Data pipelines are often complex, requiring a number of steps and dependencies. The data generated by a single application can be transferred to multiple pipelines that feed additional applications. It is crucial to be able to track these processes as well as their relationships to ensure that the pipeline works correctly.
There are three primary use cases for data pipelines: accelerating development as well as improving business intelligence and reducing risk. In each case, the goal is to collect a huge amount of data and turn it into an actionable form.
A typical data pipeline includes the series of transformations, including filtering and aggregation. Each stage of transformation will require a different data store. After all transformations have been completed and the data is moved into the destination database.
To reduce the time it takes to collect and transfer 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 deploy a data pipe quickly and easily. This will facilitate DevOps, and accelerate cloud data analysis and AI/ML projects. IBM’s patent-pending virtual data pipeline solution offers a multi-cloud copy management system that allows test and development environments to be separated from production environments. IT administrators can swiftly enable development and test by provisioning masking copies of databases on premises using an intuitive self-service GUI.