A data pipeline is a set of tools and processes used to automate the movement and transformation of data between a source system and a target repository. It is essentially the steps involved in aggregating, organizing, and moving data from one place to another. Data pipelines are used to prepare enterprise data for analysis by cleaning and refining raw data, improving its usefulness for end-users, standardizing formats for fields like dates and phone numbers while checking for input errors, removing redundancy, and ensuring consistent data quality across the organization.
Data pipelines automate many of the manual steps involved in transforming and optimizing continuous data loads, typically by loading raw data into a staging table for interim storage and then changing it before ultimately inserting it into the destination reporting tables. They can cross-check values of the same data from multiple sources and fix inconsistencies, making it easier to extract information from the data you collect fast and efficiently.
Data pipelines are used in data science projects or business intelligence dashboards to source data from various sources, transform it, and load it into a target system. They are also used to process and transform data into each service and core business application in real-time and at scale. To be effectively implemented, data pipelines need a CPU scheduling strategy to dispatch work to the available CPU cores and the usage of data structures on which the pipeline stages will operate on).
