A virtual data pipe is a set of processes that transform raw data gathered from source systems into an appropriate format that can then be accessed by applications. Pipelines can be utilized for many purposes, including analytics, reporting and machine learning. They can be configured to process data on a predefined schedule, or on demand, and may also be used for real-time processing.
Data pipelines can be complex with many steps and dependencies. For instance, the data generated by one application could be fed into several other pipelines, which then feed into other applications. The ability to track these processes, as well as their relationships with each other is crucial to ensure that the entire pipeline functions correctly.
There are three main uses instances for data pipelines – speeding up development in business intelligence, improving business efficiency and mitigating risk. In each instance, the goal is to collect a huge amount of data and transform it into actionable format.
A typical data pipeline comprises several transformations like filtering and aggregation. Each stage of transformation will require a different type of data store. After all transformations are completed and visit this site the data has been pushed into the destination database.
Virtualization is a technique used to reduce the amount of 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 quickly deploy an automated data pipeline to facilitate DevOps and speed up cloud data analytics and AI/ML efforts. The patent-pending virtual data pipe solution by IBM offers an efficient, multi-cloud copy management platform that separates development and test infrastructure from production environments. IT administrators can swiftly enable development and test by provisioning masking copies of databases on premises using the self-service GUI.