Data pipelines are one of the most critical parts of the data infrastructure. They enable data movement from source systems to destination locations, from databases to data warehouses, from APIs to data lakes, and so on. Without a proper architecture, robust monitoring, and strict SLAs, pipelines can quickly become a point of failure. Hence, data processing pipelines require careful analysis, design, and consideration.
There are different types and techniques of data pipelining. Some are built purely for data extraction and transfer, and other pipelines are also responsible for data deduplication, filtration, and transformation. There are two primary categories of full-scale pipelines: ETL, which stands for Extract, Transform and Load, and ELT, which stands for Extract Load and Transform.
The former is primarily used to put adequately formatted data into structured storage. The latter is used to set the data into storage in its raw form and transform it later. One argument, however, always stays the same: data quality will suffer without a reliability system in place.