A data pipeline is a method of moving raw data from various sources to a data store, such as a data lake or data warehouse, for analysis. It is a series of processing steps that transform and optimize data, arriving in a state that can be analyzed and used to develop business insights. Data pipelines automate many of the manual steps involved in transforming and optimizing continuous data loads, including loading raw data into a staging table for interim storage and then changing it before ultimately inserting it into the destination reporting tables.

Data pipelines are critical for real-time analytics to help organizations make faster, data-driven decisions. They are essential if an organization relies on real-time data analysis, stores data in the cloud, or houses data in multiple sources. Data pipelines clean and refine raw data, standardize formats for fields like dates and phone numbers while checking for input errors, remove redundancy, and ensure consistent data quality across the organization.

The type of data processing that a data pipeline requires is usually determined through a mix of exploratory data analysis and defined business requirements. Once the data has been appropriately filtered, merged, and summarized, it can then be stored and surfaced for use. Well-organized data pipelines provide the foundation for a range of data projects, including exploratory data analyses, data visualizations, and machine learning tasks.

In summary, a data pipeline is a series of processing steps that move, transform, or serve data, and it is critical for real-time analytics and data-driven decision-making.