What is a Data Pipeline?
A data pipeline is a means of transporting raw data from the software and database platforms to the data warehouse. Data pipelines help manage multiple data formats by identifying the right schema based on the destination of the data while transforming the source data into the appropriate format. Next, the data pipeline helps filter, validate, and transform data while ensuring that only the required data is passed on to the next step in the process. Third, a data pipeline manages data splitting, cloning, and merging. Data pipelines also help facilitate lookups and join and manage stream and batch data.
The architecture of a data pipeline is the design and structure of the code and systems that copy, cleanse, and transform data. The factors that contribute to the data pipeline are as follows:
- Throughput: The volume of data a pipeline can process within a given time.
- Reliability: The requirement of individual systems within the data pipeline to be fault-tolerant. A highly reliable data pipeline has the functions of auditing, logging, and validating to confirm data quality.
- Latency: The time needed for a single unit of data to travel through the pipeline. Lower levels of latency contribute to higher expenses of maintenance.
A data pipeline is designed and layered so that each subsystem fits into the next until data reaches the destination. Data sources are key to the pipeline’s design as they inform where the organization gathers the data daily. Next, data is ingested…