Fri. Jul 12th, 2024
data engineering

The digital world collects huge amounts of data and information every day, which are necessary for the development of companies. Not only is there an enormous amount of data, but there are also countless processes that can be applied. That’s why data analysts and data pipeline engineers are turning to data pipelining. In this article, you’ll find everything you need to know about data engineering pipelines.

Data engineering pipeline

A data pipeline is a series of data processing steps. These are the steps to move data from a location (source) to a target location (destination), such as a data warehouse. During this process, data is transformed and optimized. Then the data arrive in a form that allows for analysis and development of business insights.

The data pipeline can be compared to a public transport route. It specifies where the data enters the train and when it leaves it.

Data engineering pipeline – characteristics

Data pipelines are made up of three key elements: the source, the processing step or steps, and the destination.

Sources define where the data comes from. Examples of the most common sources are:

  • Relational database management systems
  • CRMs
  • ERPs
  • Social media management tools
  • IoT devices

After the data is collected, it is changed according to business needs. This processing includes transformation, augmentation, filtering, grouping, and aggregation. As a result of processing, data arrives at a destination, usually a data lake or warehouse.

Data engineering pipeline architecture

The data pipeline architectural infrastructure builds upon the basics to capture, organize, route, or redirect data to obtain insightful information. In addition, the pipelined infrastructure connects, adapts, automates, visualizes, transforms, and migrates data from multiple resources to achieve its goals.


A batch pipeline is a method for efficiently moving huge amounts of data and processing them in batches from the CRM system to the target system (data warehouse). This processing enables users to collect and store data during the batch window. And this, in turn, simplifies the management of a large amount of data as well as repetitive tasks. Batch processing is used in a variety of scenarios, from simple data transformations to a complete ETL pipeline.


Unlike batch processing, the streaming data pipelines flow data continuously. Through streaming pipelines, users can ingest structured and unstructured data from a wide variety of streaming sources.


The purpose of the change data capture pipeline is to update data and to keep multiple systems in sync. In this case, it is not necessary to copy the entire database. Only data changes since the last sync are shared here.

In-house vs. cloud-based data pipelines

In the past, organizations have built in-house pipelines. However, this came with a lot of challenges. Each data source provides a different API and uses different technologies. Therefore, developers have to rewrite or even create new codes. This is not only time-consuming but also costly. Therefore, building a cloud-native data pipeline allows organizations to accelerate digital transformation, reduce costs, and work more efficiently.

How does the data pipeline work?

The purpose of the data pipeline is to automate the processing of data transfer from the source system to the subordinate system. The process of developing a data pipeline includes:

  1. Defining the method and place from which the data comes,
  2. Automating the following processes: extracting, transforming, connecting, validation, data loading,
  3. Using data for operational reporting, advanced data science analytics, business analysis, and data visualization.

We can distinguish four steps in the data pipeline:

  1. Ingestion (obtaining data from various sources)
  2. Integration (data transformation)
  3. Data quality (applying data quality rules)
  4. Copying (moving the data from a data lake to a data warehouse)

What’s the difference between a data pipeline and ETL?

ETL is a common acronym used in Extract, Transform, and Load expressions. The main difference with ETL is that it focuses entirely on one system to extract, transform and load data into a specific data warehouse. Alternatively, the ETL is only one component of the data pipeline.

In the ETL process, you move data in batches to a specific system at adjustable intervals. By comparison, data pipelines have a broader application to transform and process data through streaming or real-time.

Data pipelines don’t necessarily need to load data into the data warehouse, but they can choose to load for a selective target or even connect them to a completely different system.


The amount of generated data grows every year. It is estimated that over 180 zettabytes of data will be produced by 2025.[1] It’s a lot. But fortunately, date pipelines are becoming more sophisticated. They can extract, process, and transform huge amounts of data into practical information. Consequently, your business can benefit greatly from modern data pipelines. They enable organizations to make faster decisions and gain easier access to information. To find out more, see data engineering services.