What Is a Data Pipeline?

A data pipeline is a series of data processing steps. A data pipeline might move a data set from one data storage location to another data storage location.

Written by Chris Dowsett
Published on Dec. 22, 2022
What Is a Data Pipeline?
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Data pipelines are crucial because data use in business often requires a series of calculations, data transformations and data movements in order to prepare data for use by data scientists, analysts and business intelligence teams. These calculations and transformations are called processing elements and a series of these processing elements make up a function or program called a data pipeline.

In its simplest form a data pipeline might move a data set from one data storage location to another data storage location. More complex data pipelines perform a series of processing elements such as transforming rows into columns in a table, extracting one column from the table and placing it in another table, performing a calculation on those columns and then creating a new table out of those calculations. 

Data pipelines offer a number of benefits to most businesses. For one, they reduce manual processes and ensure data moves smoothly. We can schedule pipelines to run at any time, which means we can set compute-heavy pipelines to run when employees aren’t working. This ensures pipelines aren’t taking resources away from data scientists and analysts . Finally, we can use data pipelines to ensure consistency of metrics while drawing on central data definitions and data libraries that house approved formulas.

Data Pipelines vs. ETL: What’s the Difference?

Data pipeline is the broad category of moving data from one location to another or between systems. ETL is a specific type of data pipeline, or a sub-category of data pipeline. In other words, ETL is a specific data processing workflow and type of data pipeline.

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Data Pipeline Process

Data pipelines generally consist of three broad elements in order to work and process data. 



The first is the source of the data or information. A source can be almost anything that collects data including existing data tables, cloud-based tools, CRM systems or accounting SaaS solutions, marketing sources (like social media accounts or ad networks), or broad storage solutions like Box. 



The second broad section is the processing stage. Example processing steps include transforming pieces of data, running calculations, filtering out unnecessary information, aggregating disparate pieces of data into one group or augmenting data with other information. There are many tools available that can handle processing data. Some common cloud-based data processing tools include SnowFlake, Google Cloud Platform, AWS, Segment or FiveTran.



The third section in a data pipeline workflow is the data’s destination. The destination is important because it may impact the processing stage. Example destinations include data warehouses, data storage buckets or data lakes. If the destination is a data warehouse, then the structure of the receiving data warehouse will be an important factor in setting up the processing stage of the pipeline. This is because data warehouses ingest mostly structured data and store data in preset formats. Data that’s not structured appropriately or to predetermined requirements won’t be permitted to land in the data warehouse.

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What Are the Characteristics of an Effective Data Pipeline?

Robust data pipelines can elevate data processing and remove a significant proportion of manual work, thereby ensuring you can run routine data tasks quickly, effectively and at scale. In other words, it pays to consider the characteristics of robust data pipeline infrastructure.

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The ability for a range of users to create pipelines quickly and efficiently is a key consideration for businesses setting up data pipeline infrastructure. For example, allowing data scientists to build their own data pipelines (within business specifications) removes the need for data engineers to spend time on basic pipelines, thereby freeing them up to work on more complex data engineering tasks.



The concept of elasticity means that the data pipeline tool or software can scale up to handle more complex data processing tasks, in addition to performing basic processing services as needed and consuming fewer computational resources. Data pipelines should be elastic, which means they’ll use more computational resources (e.g. number of servers assigned to the pipeline tasks) when needed and conserve computational resources for simpler data pipeline tasks when fewer resources are needed. 


Continuous Data Processing

Continuous data processing means the pipeline tool has the ability to handle sources of data that send over continuous streams of information. 


Data Source Flexibility

Modern data pipeline tools allow for easy access to a wide and ever-growing list of data sources, which may include different types of databases, marketing tools, analytics engines, SaaS tools, project management software and CRM tools among other sources. This is an important characteristic because it means more opportunity for all areas of the business to build or benefit from data pipelines. 

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