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# Data Pipelines |
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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. |
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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. |