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.
What Factors Contribute To The Data Pipeline?
3 important factors come into consideration during the creation of a data pipeline.
- Throughput: This can be defined as the rate of processing the data within a specific period.
- Reliability: The different systems that are a part of the pipeline need to have completely tolerant to different faults. Hence, the pipeline should be created with values such as validation, auditing, and logging to ensure good quality of data.
- Latency: This refers to the time taken by a single data unit to completely pass through the data pipeline without any issues.
Since huge volumes of data keep on flowing inwards every single day, it can be beneficial for an enterprise to have a properly functioning data pipeline to allow the handling of data to occur in real-time. This provides a much-needed boost to reporting factors and analytics as well. With data pipelines, the targeted functionality of the data can be increased. Also, it can help with process automation, target customer behavior analysis, and other important processes.
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