A data pipeline is a sequence of steps that collect, process, and move data between sources for storage, analytics, machine learning, or other uses. For example, data pipelines are often used to send data from applications to storage devices like data warehouses or data lakes. Data pipelines are also frequently used to pull data from storage and transform it into a format that is useful for analytics.
Data pipelines generally consist of three overall steps: extraction, transformation, and loading. These steps can be executed in different orders, such as ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform). In either case, pipelines are used to extract and transform data to achieve business goals.
The series of transformations required to execute an effective pipeline can be very complex. For that reason, specialized software is often used to aid and automate the data pipelining process.
Key characteristics of data pipelines:
Extraction: Extraction involves collecting data from various sources such as databases, APIs, files, or applications. This step identifies and retrieves the data that needs to be processed.
Transformation: Transformation involves converting data into the required format, cleaning invalid or duplicate records, validating data quality, and enriching data with additional information from other sources.
Loading: Loading moves the processed data to destination systems like data warehouses, data lakes, or analytics platforms where it can be accessed for business intelligence, reporting, or further analysis.
Automation and Reliability: Modern data pipelines support automation, enabling continuous, scheduled, or real-time data processing. They also include error handling, retry mechanisms, and data quality checks to ensure reliability throughout the pipeline.
Use Cases: Data pipelines are essential for: - ETL/ELT processes to move data from operational systems to analytical systems - Real-time data processing for applications that require up-to-date information - Data integration across multiple systems and platforms - Data preparation for machine learning model training - Business intelligence and reporting workflows