An automated series of steps that moves data from one or more sources, transforms it, and delivers it to a destination for use.
A data pipeline collects data from places such as applications, databases, files, or sensors, then cleans, combines, or reshapes it, and loads it into a destination such as a data warehouse, dashboard, or machine learning system. Pipelines can run in scheduled batches, for example every night, or continuously as streaming pipelines.
For example, a chain of clinics might build a pipeline that pulls appointment records from its booking system and payments from its billing software every hour, standardizes dates and clinic names, and loads the results into a reporting database. ETL and ELT are common pipeline patterns. Reliable pipelines need monitoring, error handling, and data quality checks, because a silent failure can leave decision-makers looking at outdated or incorrect numbers.
Finding and correcting errors, duplicates, and inconsistencies in data so it is accurate and usable for analysis.
Data WarehouseA system that stores cleaned, structured data from many sources, optimized for reporting and business analysis.
ETLExtract, Transform, Load: a process that pulls data from sources, cleans and reshapes it, and loads it into a destination such as a data warehouse.
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