Data Pipeline: Definition, Examples & Types

Data pipeline management begins with data ingestion from various sources (e.g. external APIs, physical devices, databases), often in the form of both structured and unstructured data.

Data processing engines transform, clean, enrich, and filter the data based on predetermined rules and logic. In some cases, ETL processes may be used. 

Data pipeline management ends with processed data being stored in repositories such as data warehouses, data sinks, and cloud-based solutions. This processed data is now ready for further analysis and business intelligence insights.

Not exactly. ETL data pipelines are one type of data pipeline. The term “data pipeline” is a very broad category – which may or may not include ETL processes – as there are additional ways to move data from point A to point B.

Not all data pipelines use the ETL process. In some data pipelines, data is not processed or transformed prior to being loaded into its final destination.

When designing and implementing a big data pipeline, several key factors must be considered:

• Scalability: Pipelines must be able to manage increasing data volumes and processing demands.
• Flexibility: Pipelines should be able to process diverse data formats, including structured, unstructured, and semi-structured data.
• Security: Sensitive data must be reliably protected throughout the pipeline.
• Fault and Risk Tolerance: Pipelines should be able to recover from failures, risks, and errors.
• Metadata Management: Pipelines must be able to effectively track data lineage and quality.
• Performance: Pipelines should be optimized for performance, especially in time-sensitive applications and if processing must be done during peak hours.

Modern data pipelines are automated, cloud-based systems that specialize in ingesting, processing, and storing massive amounts of data.

They are often characterized by continuous, real-time, or near-real-time processing, cloud-based architectures, self-service capabilities, business continuity, and adaptable disaster recovery.