Data Engineering Guide to Build Strong Azure Data Pipeline

This data engineering guide is useful for understanding the concept of building a strong data pipeline in Azure. Information nowadays is considered the main indicator in decision-making, business practice, and analysis, still, the main issue is having this data collected, processed, and maintained effectively and properly. Azure Data Engineering, Microsoft's cloud services, offers solutions that help businesses design massive amounts of information transport pipelines in a structured, secure way. In this guide, we will shed light on how building an effective information pipeline can be achieved using Azure Data Engineering services even if it was your first time in this area.
Explain the concept of a data pipeline.
A data pipeline refers to an automated system. It transmits unstructured information from a source to a designated endpoint where it can be eventually stored and analyzed. Data is collected from different sources, such as applications, files, archives, databases, and services, and may be converted into a generic format, processed, and then transferred to the endpoint. Pipelines facilitate the smooth flow of information and the automation of such a process helps to keep the system under control by avoiding any human intervention and lets it process information in real-time or in batches at established intervals. Finally, the pipeline can handle an extremely high volume of data while tracking workflow actions comprehensively and reliably. This is essential for data-driven business processes that rely on huge amounts of information.