What is a data pipeline?

Data pipelines are essential for transforming raw data into valuable business insights by executing a series of processing steps that move data from one location to another. They form a crucial component of data management and analytics infrastructure, capable of handling data from various sources, whether on-premise or cloud-based, and ensuring compliance, improved data quality, and reduced latency in data consumption. Despite their benefits, such as automation, enhanced data quality, and compliance management, data pipelines present challenges like cost, technical complexity, and data security issues. They generally consist of three stages: data ingestion, processing, and delivery, and can be structured using scripting languages like Python or SQL, with tools like Starburst, dbt, and Apache Airflow facilitating their orchestration and management. The choice between ETL, ELT, streaming, and batch pipelines depends on specific organizational needs, and while they can streamline data governance, they require careful management to avoid redundant or noncompliant pipelines.

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