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The Data Processing Pipeline: Top Benefits of Automated and Efficient Data Management

 Engineers control the flow of data through a data processing pipeline.

Data is a precious commodity and your organization likely stores, handles, and transports a large volume of it every single day. A data processing pipeline is an organized, programmatic method of getting data from its source to its destination(s) while making any necessary transformations along the way. Modern data processing pipelines employ automation to streamline the flow of data and create flexible and scalable pipeline architectures. In this blog, we’ll look at the components of a typical data processing pipeline before explaining the benefits of an automated approach to data management.

What Does a Data Processing Pipeline Look Like?

A basic data processing pipeline typically includes the following stages:

Discovering and Analyzing Data Sources

Data sources are the origination points of the data flowing through your pipeline, including databases, SaaS applications, storage systems, etc. An automated data processing pipeline often uses data discovery or data profiling tools to locate and analyze data across your data sources before it’s pulled into the pipeline. That allows for automated, intelligent processing of data in later stages of the pipeline according to characteristics, such as the data’s structure, value, and risk.


The ingestion stage pulls data from your data sources into the pipeline. It involves processes and technologies like API calls, replication engines, and webhooks. There are generally two methods to ingest data into a data processing pipeline:

  1. Batch ingestion: Data enters the pipeline as a group according to a schedule or in response to external triggers.
  2. Streaming ingestion: Individual data enters the pipeline in real time.


Often, raw data in a pipeline needs to be reformatted or altered in some way to make it compatible with its destination, a process known as transformation. Transformation may occur before the data reaches its destination in an ETL — or extract, transform, load — pipeline. ETLs are commonly used for on-premises data destinations. Data processing pipelines with cloud-based destinations often use ELT — or extract, load, transform — which first loads data to its final destination and then applies transformations.


The pipeline delivers data to its final destination. This happens either before data transformation, in the case of an ETL pipeline, or after with an ELT pipeline. Often, the destination is a data lake or data warehouse. These locations store massive amounts of data for analytics, machine learning, and other big data applications. However, pipelines frequently deliver smaller amounts of data to another application, or even another microservice within the same application. A pipeline may even have the same source and destination. In this case, it serves purely to automatically process and transform the data.


It’s not enough to deliver data to its destination — it needs to be put into a format people can actually use. Dashboard and reporting tools turn data into business insights. 

Data processing pipelines take data from point A to point B and facilitate efficient, automatic data transformation and processing.

The Top Benefits of a Data Processing Pipeline

At a very basic level, the data processing pipeline breaks down data transfer and processing into a series of programmatic steps. This has numerous advantages, including:

  • Automation. Breaking a process down into smaller, repeatable steps facilitates the use of automation. Automating steps in your pipeline will help reduce the human bottlenecks between stages so you can process data much faster. Automation can also consistently apply the correct data governance policies to everything that passes through your pipeline.
  • Efficiency. An automated data pipeline can transport and transform massive amounts of data very quickly. It can also process multiple parallel data streams at the same time. Automated data processing also weeds out redundant and erroneous data, which means your applications and analytics work more efficiently at the end of the pipeline.
  • Flexibility. The data you need to process may come from many different sources and have a variety of different formats and characteristics. A data processing pipeline allows you to work with a large variety of data without worrying about compatibility with destination storage volumes or applications. Automated data pipelines also make it easier to adapt to changes in the pipeline itself, such as new data sources or transformation processes.
  • Analytics. Processing pipelines aggregate and prepare data for analytics so you can get fast and reliable business insights. Additionally, a data processing pipeline enables security and compliance best practices, so you can make use of analytics without exposing sensitive data. 
  • Value. A data processing pipeline allows you to extract more value out of your data by facilitating business analytics, machine learning, and other data science applications. These tools can provide data analysis and visualizations that give you actionable insights, empowering your organization to spot new opportunities, improve operational processes, and make better business decisions.

Implementing a Data Processing Pipeline for Automated and Efficient Data Management

A data processing pipeline helps you manage, transport, and transform data more efficiently. If you need help building a data processing pipeline that follows industry best practices, you should reach out to the experts at Copado Strategic Services. We’ll support you throughout the data pipeline implementation process, so you get an end product that delivers automated and efficient data management.