What is a Virtual Data Pipeline?

A virtual data pipe is a set processes which transform raw data from sources into an appropriate format that can be consumed by software. Pipelines can be used for a variety of purposes, including analytics, reporting, and machine learning. They can be configured to run data on a timetable or on demand. They can also be used for real-time processing.

Data pipelines are often complex with many steps and dependencies. For instance, the data generated by one application could be fed into multiple other pipelines, and then feed into other applications. It is vital to be able to track these click for source processes and their connections to ensure that the pipeline operates properly.

There are three primary uses cases for data pipelines: the speed of development as well as improving business intelligence and reducing risk. In each of these cases it is the intention to gather a large amount of data and transform it into a format that can be used.

A typical data pipeline will include many transformations such as reduction, aggregation, and filtering. Each stage of transformation will require a different data store. Once all the transformations are finished, the data will be pushed into its destination database.

To reduce the time it takes to store 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.

With IBM Cloud Pak for Data, powered by Actifio, you can easily set up a virtual data pipeline that will facilitate DevOps and accelerate cloud data analytics and AI/ML efforts. IBM’s patented virtual data pipeline solution provides a multi-cloud copy management system that allows test and development environments to be decoupled from production environments. IT administrators can quickly enable test and development by setting up masking copies of databases on premises using an easy-to-use GUI.