Data pipeline consists of a set of actions performed in real-time or in batches, that captures data from various sources, sorting it and then moving that data through applications, filters, and APIs for storage and analysis.
For instance, a data pipeline can be a simple process allowing the flow of data from an application to a data warehouse, or an advanced process designed to handle data from a data lake to an analytics database, using machine learning or predictive analysis.
To put it into practice, businesses can benefit from a data pipeline for strategic purposes, through the automation, management, visualization, transformation, and movement of data from multiple sources. This way, data pipeline architecture is primarily applied to help data improve specific functionalities and business analytics.
EAI cares about the efficiency of their pipelines using strategies to avoid corrupted data, hitting bottlenecks (causing latency), a conflict between data sources or duplicates among other issues. Business size or industry may not directly impact the complexity of a data pipeline.