What Is A Data Pipeline?

A data pipeline is the means by which data travels from one place to another within an organization’s tech stack. It can include any building or processing block that assists with moving data from one end to another.

Data pipelines typically consist of:

Sources, such as SaaS applications and databases.
Processing, or what happens to the data as it moves through the pipeline from one place to another, including transformation (i.e., standardization, sorting, deduplication, and validation), verification, augmentation, filtering, grouping, and aggregation.
Destinations, which are most commonly datastores such as data warehouses and data lakes.

Typical data pipeline use cases include:

Predictive analytics
Real-time dashboards and reporting
Storing, enriching, moving, or transforming data

Data pipelines can be built in-house but are now more commonly built in the cloud because of the elasticity and flexibility it provides.

Benefits of a Data Pipeline

A data pipeline allows organizations to optimize their data and maximize its value by manipulating it in ways that benefit the business. For example, a company that develops and sells an application for automating stoplights in large cities might use its data pipeline to train data sets for machine learning so that the application can then work optimally for the cities, allowing stoplights to move traffic efficiently through streets.

The primary benefits of a data pipeline are:

Data analysis: Date pipelines enable organizations to analyze their data by collecting data from multiple sources and putting it all into a single place. Ideally, this analysis is taking place in real time to extract the maximum value from the data.
Elimination of bottlenecks: Data pipelines ensure a smooth flow of data from one place to another, thus avoiding the issue of data silos and eliminating the bottlenecks that lead to data rapidly losing its value or getting corrupted in some way.
Better business decisions: By enabling data analysis and eliminating bottlenecks, data pipelines give businesses the ability to use their data for quick and powerful business insights.

Importance of Automation and Orchestration for Data Pipelines

Automation and orchestration are critical aspects of data pipelines. Data pipeline automation is the ability to run any of the data pipeline’s components at the time and speed at which you need them to run. Data pipeline orchestration is the process of running all of the components in a coordinated manner.

Full data pipeline automation enables organizations to seamlessly integrate data from various sources to fuel business applications and data analytics, quickly crunch real-time data to drive better business decisions, and easily scale cloud-based solutions.

Orchestration enables DataOps teams to centralize the management and control of end-to-end data pipelines. It allows them to perform monitoring and reporting and get proactive alerts.

Data Pipelines vs. ETL

Like data pipelines, extract, transform, and load (ETL) systems, also known as ETL pipelines, take data from one place to another.

However, unlike data pipelines, ETL pipelines, by definition:

Always involve transforming the data in some way, while a data pipeline doesn’t always necessarily have to involve transforming the data.
Run in batches where data is moved in chunks, while data pipelines run in real time.
End with loading the data into a database or data warehouse, while a data pipeline doesn’t always have to end with data loading. It can instead end with the activation of a new process or flow by triggering webhooks.

ETL systems are typically, but not always, subsets of data pipelines.

How to Make the Most of Your Data Pipeline

A data pipeline is only as efficient and effective as its constituent parts. A single weak or broken link can break your entire pipeline and lead to a large amount of lost investment and time.

That’s why today’s enterprises are looking for solutions that help them make the most of their data without adding significant costs.

A data storage solution such as a unified fast file and object (UFFO) storage platform consolidates all data—both structured and unstructured—into a central accessible data layer. In contrast to a data warehouse, it can handle operational data, and unlike a data lake, it can serve data in multiple formats.

A UFFO storage platform can also consolidate data lakes and data warehouses into a single access layer and provide the data governance needed to streamline data sharing between a diverse collection of endpoints. With a data hub, the data processing is abstracted away, giving your organization a centralized place from which to extract business intelligence (BI) insights.

Pure Storage® FlashBlade® is the industry’s leading UFFO storage platform. FlashBlade can not only handle the analytics and reporting workloads of a data warehouse but also deliver: