What is a Data Pipeline?
A data pipeline is, effectively, a set of processing steps. It is a means of moving data from one location (the source) to another (the destination, such as a database or data warehouse). But data pipelines aren’t simply conduits that data travels through. The process also involves transforming and optimizing the data set so that it can be processed and analyzed to generate valuable business insights.
While you may imagine a data pipeline the same way you do critical infrastructure such as an oil or main water line, the term more accurately refers to the steps required to aggregate, organize, and move data. Data pipeline architecture automates the transformation and optimization of continuous data loads, replacing many otherwise manual steps.
Data Pipeline or ETL Pipeline: What’s the Difference?
While both are used for transforming and moving data, there are key differences between data pipelines and ETL pipelines. The former can include the latter, but the opposite is not true.
A data pipeline is a broad term referring to any process or tool for transforming and moving data. This can include ETL pipelines, data streaming , real-time data processing, and other methods for data transformation. Generally speaking, data pipelines are used by organizations seeking real-time data analysis by processing and parsing data when it is generated.
An ETL pipeline is a specific process for extracting data from single or multiple sources, transforming it into a consistent format, and then moving it to a destination location such as a database or data warehouse. ETL – extract, transform, load – refers to the three stages of an ETL pipeline.
Data pipelines handle a wide variety of data types, while ETL pipelines typically address structured, relational data and often use SQL-based tools. Ultimately, ETL is a sub-process of the broader scheme of data pipelines in big data and event-based records.
Case Study: Risk Monitoring in Fortune-100 Financial Firm
DownloadBenefits of a Data Pipeline Architecture
When determining the best approach for your organization, it helps to understand the benefits of a data integration pipeline. It’s worth noting that data pipelines can be used in a continuous or streaming implementation or with batch processing. Whether you opt for streaming or batch, a data pipeline offers numerous benefits, including:
Agility and Flexibility
Data pipelines mobilize data while ensuring that up-to-date information is available at any given time. With a global workforce moving to hybrid and remote models and increasing reliance on cloud tools, cloud data pipelines provide elasticity and agility without traditional pipelines’ high financial and resource costs.
Decision-making and Collaboration
Data is king, but it’s only as powerful as the insights derived from it. With well-designed data pipeline management, data is collected with a single source of truth – a central repository that can be analyzed to generate insights. All organization members will access the same data, allowing cross-departmental collaboration.
Data Quality
A crucial aspect of the data pipeline flow is cleaning and refining data. Through these steps, data quality is improved and, therefore, more valuable to end users. Data pipelines also standardize reporting and analyses, enabling your organization to process and collect data consistently. This eliminates inconsistent metrics and formula errors that may otherwise downgrade data quality.
