Data Pipeline
- Automates moving and preparing data so it can be analyzed or used by machine learning systems.
- Supports both batch workflows (e.g., ETL) and real-time processing (streaming).
- Enables integration of data from multiple sources and regular processing (for example, extracting data from a web log every hour).
Definition
Section titled “Definition”A data pipeline is a set of processes that move data from one place to another. This typically involves extracting data from a source, transforming it in some way, and then loading it into a destination such as a database or data warehouse.
Explanation
Section titled “Explanation”Data pipelines perform the recurring tasks required to make data available for analytics and machine learning. They can take many forms: some pipelines collect and harmonize data from multiple sources into a central repository, while others process and analyze data as it is generated. By automating extraction, transformation, and loading steps, pipelines reduce manual work, save time and resources, and help organizations gain insights from data more quickly and accurately. Pipelines are especially useful when data is dispersed across different systems and formats, as they enable integration to provide a more comprehensive view.
Examples
Section titled “Examples”Extract, Transform, Load (ETL) pipelines
Section titled “Extract, Transform, Load (ETL) pipelines”These pipelines extract data from multiple sources, transform it into a format suitable for analysis, and load it into a destination such as a data warehouse. For example, an ETL pipeline might extract data from multiple sources such as web logs, social media feeds, and transactional systems, transform it into a consistent format, and then load it into a data warehouse for further analysis. An ETL pipeline might also be used to extract data from a web log every hour, transform it into a consistent format, and then load it into a data warehouse for further analysis.
Real-time streaming pipelines
Section titled “Real-time streaming pipelines”These pipelines process and analyze data in real time as it is generated. For example, a streaming pipeline might process data from sensors in an industrial plant to monitor equipment performance and detect potential failures. The pipeline might use machine learning algorithms to analyze the data in real time and generate alerts or take other actions as needed.
Use cases
Section titled “Use cases”- Data analytics and machine learning applications that require large-scale data processing.
- Monitoring equipment performance and detecting potential failures using sensor data.
- Integrating data from multiple sources to build a comprehensive view across systems and formats.
- Automating regular data processing tasks (for example, periodic batch extracts such as every hour).
Related terms
Section titled “Related terms”- Extract, Transform, Load (ETL)
- Real-time streaming pipelines
- Data warehouse
- Data analytics
- Machine learning
- Sensors
- Web logs
- Social media feeds
- Transactional systems