What Is A Data Pipeline? Guide With Examples

Luke Smith
Enterprise Solutions Architect
November 4, 2022
Matt Tanner
Developer Relations Lead
November 4, 2022
Matt Tanner
Matt Tanner
Developer Relations Lead
Developer Relations Lead
February 6, 2023
 min read
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It seems that every organization these days seeks to integrate data they produce from multiple sources and in various formats into a unified storage location. Although many reasons can be found for this desire, analytics is one of the top use cases. This makes sense since analytics become much more useful when larger amounts of data can be funneled in and taken into the equation. The role of data pipelines in this effort is paramount to the data and business teams implementing and using these analytics. 

It has been established that thriving in today’s world requires the creation of modern data pipelines. The modern data pipeline aims to make it easy to move data, with less cost, and gain valuable insights from it. With modern technologies, organizations can have thousands of intelligent data pipelines that can move data from specialized locations to target systems and applications.

To become competitive, building an effective data pipeline, or multiple, is a critical step. To do this, the businesses must plan and develop a plan to move the data generated from one source system or application and move it to a target platform. This data may also be used to feed other data pipelines. For a greater understanding of the data pipeline and data pipeline tools, let's dive into the details.

Table of Contents

What Is a Data Pipeline?

In its simplest form, a data pipeline is a combination of data processing steps used to move data from a source to a destination. It is a process in which each step’s output provides the input for the next step. These steps include where data is transformed and optimized to a required state that meets the specification of the destination. These steps can be as simple or complex as needed.

The data pipeline activities involve data ingestion at the beginning of the pipeline from one or multiple disparate sources. Then, several processing steps like aggregation, filtering, and organizing take place before the data is moved to the destination system. This is where the data is analyzed and business insights are gathered.

The data pipeline itself is an automated process and may be used for extracting data and loading it to the destination. More complex data pipelines may be designed to handle and format data for advanced applications such as training datasets for machine learning.

Data Pipeline vs ETL

Extract, transform, and Load (ETL) refers to a specific type of data pipeline. It is a subcategory of a data pipeline that follows a specific sequence. The extract step refers to the extraction of data from the source system. The next step, transform, is where the data is modified in a staging area to meet the destination’s requirements. The last step, load, is where the reformatted data is inserted into the final storage destination.

On the other hand, a data pipeline is more complex and broader as it covers the entire process of moving data from one location to another. For example, some data pipelines may not even require a transformation stage. In these cases, data can be moved from the source to the target system without any changes. This is true if the destination system supports the same data format as the source. This may also be the case when data transformation can occur after data has been loaded to the destination.

Data Pipeline Considerations

Before you proceed with building or deploying a data pipeline, you have to first understand a few key considerations. These considerations should take into account:

  • Your business objectives
  • The data sources and destinations that need to be supported
  • Other tools you will need in order to ensure a successful implementation

These considerations are crucial to ensure a properly constructed data pipeline. Taking into consideration these factors can help productivity since a poorly designed and built data pipeline can be a major hindrance.

When it comes to designing the data pipeline, to overcome any challenge that may arise, run through the following considerations:

  • Determine the type of data the data pipeline will handle and if it can be optimized for stream processing.
  • The rate of expected data and the overall volume.
  • How much processing will need to be done on the data?
  • The tools or technologies that can be used in achieving this.
  • Where the data will be sourced from. Will it be from an on-premise or cloud?
  • The storage location/type of the destination system.
  • The knowledge and capabilities of your team to implement and maintain the data pipeline.

Elements Of A Data Pipeline

The data pipeline is made up of three basic features namely the source or sources, processing steps, and the destination.


This is the source of the data. Data can originate from various sources such as RDBMS like MySQL, CRMs like HubSpot and Salesforce, ERPs like Oracle and SAP, social media, APIs, IoT devices, or even public datasets.

Processing Steps 

This stage is where data is processed before final loading into the destination. The business needs of the organization usually determine the various processing steps. Data can be altered and transformed to reflect augmentation, filtering, aggregation, and lots more.


Also, can be called a target. This is the final resting place of the data at the end of processing. It is also known as a sink, which could be a data lake or a data warehouse.

What Are The Different Types Of Data Pipelines?

There are generally two types of data pipeline operating modes. All data pipelines can be classified into these two categories below:

  • Batch: Batch processing is when data or large volumes of data are moved from a source to the target at a regularly scheduled interval. It can be done at any time but often occurs during off-peak business hours so that other workloads are not impacted. This is especially true if large volumes of data need to be moved to minimize system impact. This style can be deployed when there is no immediate need to analyze a specific dataset. For example, non-essential data that are not time-sensitive can be integrated with a larger system at regular intervals for analysis.
  • Real-time or streaming: Unlike batch processing, in real-time or streaming pipelines data is ingested in real-time. Sources feeding the data pipeline may be from streaming sources such as data from sensors, IoT devices, and financial markets so the data can be continuously updated. It captures data as it originates from the source system in real time, performs some transformation, then transfers it to the downstream process. For example, applications or point-of-sale systems need real-time data to constantly update inventory and sales history.

Data Pipeline Architecture Examples

A data pipeline architecture is used to describe the arrangement of the components for the extraction, processing, and moving of data. Below is a description of the various types to help you decide on one that will meet your goals and objectives:

  • ETL data pipeline: This is the most common data pipeline architecture. As explained earlier, it extracts raw data from multiple sources, transforms it into a pre-defined format, and then loads it into the storage location, mainly a data warehouse or data mart. The ETL data pipeline architecture has to be rebuilt each time there is a need to change the data format.
  • ELT data pipeline: The difference between the ELT and ETL data pipeline architecture is the arrangement of sequence. In the ETL pipeline architecture, transformation is carried out on the data so it matches the format on the destination before loading. In the ELT data pipeline architecture, large amounts of raw data can be sent directly to the data warehouse or data lake and then transformed into the desired format. This is useful when you are unsure what you will use the data for or how you want to transform it. With this architecture, transformation can be done partially, entirely, once, or as many times as you require without having to rebuild.
  • Batch pipeline architecture: This is when data is collected, processed, and moved to a location in large blocks known as batches. This could happen as soon as updates occur or at specified intervals. It is applicable in traditional analytics that is not time sensitive and doesn’t require real-time insights. The data can then be used for analytics using various business intelligence tools. 
  • Streaming data pipeline architecture: This architecture is used when you want to deduce insights from a continuous data flow in real time. The streaming pipeline ingests the new data as it is produced and updates the data in response to every event that takes place. The streaming data architecture provides businesses with up-to-date information about ongoing operations. It can be used to monitor, inform business decisions, and improve the performance of the business using real-time analytics.
  • Lambda architecture:  Lambda data pipeline architecture is useful for big data analytics. They are used to performing the same tasks as other architectures but can handle massive volumes of data coming from multiple sources. Each data source may have diverse formats and require data to still be handled at high speeds. To analyze big data operations successfully, organizations typically involve the use of both batch and real-time pipelines. They leverage the characteristics of both systems to provide an effective method to extract, transform, and load data. The Lambda architecture allows data to be stored in its raw format where new data pipelines can be created to correct any errors that may have arisen in previous pipelines. This type also makes it easy to add new data destinations that can be used to enable new types of queries.

Benefits of a Data Pipeline

Several benefits can be seen when businesses create robust data pipelines. Below are some of these benefits:

  • It provides a single view of the dataset. Data is moved from various sources to the target system for comprehensive analysis in a single place.
  • It provides a smooth and automated process for gathering, transforming, and moving data from one stage to another. Pipelines improve and enhance the flow of data throughout your organization.
  • Consistent data quality is assured as all your data is stored in a single source of truth. This empowers quick and reliable data analysis for business insights.
  • As the volume, variety, and velocity of data grow, data pipelines can scale depending on your needs. This can be seen within the cloud and hybrid cloud environments especially.

Using Arcion for Easy and Robust Data Pipeline Creation

When it comes to creating data pipelines easily, the right too can make all the difference. Of all the tools available, we built Arcion so that it is much simpler to implement and maintain than other tools and approaches. 

Arcion is a go-to solution for many enterprises who are looking to select a data pipeline tool that is scalable, reliable, and extremely easy to configure and use. It provides robust data pipelines that offer high availability, streaming capabilities through log-based CDC, and auto-scalable features. Available with multiple deployment options, Arcion can migrate data to and from on-prem data sources, cloud-based data sources or a mix of both. 

The zero-code approach allows users to easily configure Arcion and build their data pipelines without writing any code. Arcion can be set up and configured strictly through configuration files or by using Arcion’s intuitive and easy-to-use UI to set up pipelines in a matter of minutes. Compared to homegrown solutions or ones that mismatch a bunch of different technologies, Arcion makes implementation smooth by providing 24/7 support through extensive documentation, tutorials, blogs, and customer support.

Let’s take a look at some specific features that will benefit you while building data pipelines with Arcion.

Sub-Second Latency 

Many other existing data pipeline solutions don’t scale for high-volume, high-velocity data. This results in slow pipelines and slow delivery to the target systems. Arcion is the only distributed, end-to-end multi-threaded CDC solution that auto-scales vertically & horizontally. Any process that runs on Source & Target is parallelized using patent-pending techniques to achieve maximum throughput. There isn’t a single step within the pipeline that is single-threaded. It means Arcion users get ultra-low latency CDC replication and can always keep up with the forever-increasing data volume on Source.

100% Agentless Change Data Capture

Arcion is the only CDC vendor in the market that offers 100% agentless CDC to all its supported 20+ connectors. Arcion reads directly from database logs, never reading from the database itself. Previously, data teams faced administrative nightmares and security risks associated with running agent-based software in production environments. You can now replicate data in real-time, at scale, with guaranteed delivery - but without the inherent performance issues or the security concerns of having to install an agent to extract data from your pipeline sources.

Data Consistency Guaranteed

Arcion provides transactional integrity and data consistency through its CDC technology. To further this effort, Arcion also has built-in data validation support that works automatically and efficiently to ensure data integrity is always maintained. It offers a solution for both scalable data migration and replication while making sure that zero data loss has occurred.

Automatic Schema Conversion & Schema Evolution Support

Arcion handles schema changes out of the box requiring no user intervention. This helps mitigate data loss and eliminate downtime caused by pipeline-breaking schema changes. This is possible by intercepting changes in the source database and propagating them while ensuring compatibility with the target's schema evolution. Other solutions would reload (re-do the snapshot) the data when there is a schema change in the source databases, which causes pipeline downtime and requires a lot of computing resources (expensive!).   

Pre-Built 20+ Enterprise Data Connectors

Arcion has a large library of pre-built data connectors. These connectors can provide support for almost 20 enterprise databases, data warehouses, and cloud-native analytics platforms (see full list). Unlike other ETL tools, Arcion provides full control over data while still maintaining a high degree of automation. Data can be moved from one source to multiple targets or multiple sources to a single target depending on your use case. This means that if you branch out into other technologies, you’ll already have the capability within Arcion to handle your new sources and targets without the need for another pipeline technology.


This article has covered a lot about data pipelines and their use cases. We showed how a data pipeline allows you to move data into your data lake or data warehouse, which can be batched or continuous. We also covered how processing can either take place before the transfer of data to the destination or after the transfer. We also discussed the types and many benefits of different pipeline technologies.

Building a data pipeline may not be an easy thing to do if you are not familiar with the skills and technologies. Therefore, if you are struggling to build a data pipeline or do not know the approach to use, it may be better to talk to a team familiar with data engineering solutions. This is where

comes into play. As we discussed, Arcion is a real time data platform and ELT tool that can be used to handle all your data needs. It allows for data integration from multiple sources through simple configuration files or an elegant User Interface. 

Download Arcion Self-hosted for free and see Arcion in action yourself (no payment info required). If you prefer, you can also book a personal demo with our database experts today to get started! 

Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
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