Data Pipeline
In data engineering and machine learning, a data pipeline refers to the steps involved in extracting, transforming, loading, and processing data. The goal of a data pipeline is to automate the flow of data from raw data sources to the final storage location, enabling organizations to quickly and easily access and analyze their data.
Data pipelines can be built using various tools and technologies, including batch processing frameworks, data processing engines, and cloud-based data platforms. Having an effective data pipeline can help organizations improve the quality and accuracy of their data, gain insights and make better decisions, and increase the efficiency and speed of their data processing.