# Data Pipelines

Data pipelines refer to a set of processes that involve moving data from one system to another, for purposes such as data integration, data migration, data transformation, or data synchronization. These processes can involve a variety of data sources and destinations, and may often require data to be cleaned, enriched, or otherwise transformed along the way. It's a key concept in data engineering to ensure that data is appropriately processed from its source to the location where it will be used, typically a data warehouse, data mart, or a data lake. As such, data pipelines play a crucial part in building an effective and efficient data analytics setup, enabling the flow of data to be processed for insights.

It is important to understand the difference between ELT and ETL pipelines. ELT stands for Extract, Load, Transform, and refers to a process where data is first extracted from source systems, then loaded into a target system, and finally transformed within the target system. ETL, on the other hand, stands for Extract, Transform, Load, and refers to a process where data is first extracted from source systems, then transformed, and finally loaded into a target system. The choice between ELT and ETL pipelines depends on the specific requirements of the data processing tasks at hand, and the capabilities of the systems involved.