Data Processing Pipelines

- Overview

Data pipelines and data processing pipelines are both used to move and process data, but they have different purposes and characteristics: 

• Data pipeline: A general term that refers to any process that moves and processes data, such as data integration, migration, synchronization, and processing for machine learning (ML) and artificial intelligence (AI). Data pipelines can handle both structured and unstructured data in real-time.
• Data processing pipeline: A pipeline that automates data workflows to reduce manual effort and increase efficiency in data processing. Data processing pipelines can extract data from various sources, transform it into a unified format, and load it into a data warehouse.
• ETL pipeline: A type of data pipeline that focuses on extracting data from various sources, transforming it into a suitable format, and loading it into a target system. ETL pipelines are specifically designed for data warehousing and business intelligence applications.

Data pipelines are important because they automate the process of moving data from source to destination, which saves time and reduces the likelihood of human error. 

ETL pipelines are specifically designed to extract, transform, and load data into a target system (such as a data warehouse or cloud data platform) to prepare data for analysis. In contrast, data pipelines focus on moving data from one system to another, often without transforming it.