For every single enterprise trying to generate value from the compiled data, it is important to have proper management of information flow from the source to the destination, like a data warehouse. This task proves to be an intricate and complicated one since there are so many things that could go wrong. Errors could propagate along the pathway of the source and destination or there might be duplication or corruption of data. With an increase in the data volume and the sources, the process gets even more complex. This is where data pipelines can help. With data pipeline automation, the flow of information can be simplified by eliminating all the manual steps in the process.
What Exactly Does Data Pipeline Architecture Mean?
A data pipeline architecture can be defined as a particular arrangement of objects that will regulate, extract, and route the data to various relevant systems to gain valuable insights. While pipelines of big data and ETL tend to extract the data from the source and transform it for loading it into the system, the data pipeline has a much more simplified process. It embraces all the values of the big data and ETL pipelines into one singular subset. One of the main differences between data pipeline and ETL is that the former tend to use proper processing tools to transport the data from a particular system to another one without the transformation taking place.