ETL vs Data Pipeline: What is the Difference?

ETL (Extract, Transform, and Load) and data pipelines are both methods used in the kind of large-scale data processing and analysis businesses rely on to make informed decisions. ETL is a process to consolidate data from a variety of sources into a unified format. Similarly, a data pipeline primes data from multiple sources and in different formats for storage and analysis—in short, it’s a comprehensive system of connected processing steps that leads to a centralized destination. Each concept has unique core functionalities and applications. This article explains their specifics to provide an understanding of how they work and their key differences.

Jump to:

• What is ETL?
• What is a Data Pipeline?
• ETL vs. Data Pipeline: Key Differences
• Transformation Process: ETL vs. Data Pipelines
• Examples of ETL vs. Data Pipeline
• Tools and Technologies: Popular ETL and Data Pipeline Platforms
• Bottom Line: ETL vs. Data Pipeline

What is ETL?

Extract, Transform, and Load, or ETL, refers to a three-step process used in data management and warehousing during which data gets consolidated from a variety of sources and transformed into a single, unified format that aligns with business rules. Together, these steps prepare the data for refinement and further analysis for business intelligence (BI) purposes while ensuring that data quality remains uncompromised.

• Extract. Data is sourced from various databases and platforms from around the organization’s ecosystem. The format may differ depending on its source, from structured data found in SQL databases to unstructured data like weblogs and social media feeds.
• Transform. In this refinement and modification stage, data is converted and restructured into the desired format. This includes a wide range of processes to clean and deduplicate the data and address any missing or null values it contains.
• Load. Data is inserted into a target database or data warehouse for long-term storage and accessibility where it can be queried and analyzed as needed.

When to Use ETL

ETL is indispensable in data management. For example, it ensures consistency and accuracy when attempting to handle large volumes of data being rapidly updated and moved around. ETL tools can address data anomalies early in the process, creating a more standardized format of the data and streamlining analysis.

It’s not just a technical process—it’s also a strategic one. ETL is often used in large-scale data migration projects where organizations need to transform data from legacy systems to suit their management and storage solutions. ETL can ensure that the data is clean, well-integrated, and actionable.

What is a Data Pipeline?

A data pipeline is a comprehensive system of processing elements and steps leading to a centralized destination that primes data from multiple sources and in a wide range of formats for storage and analysis. Typically, data pipelines help with error handling and data integration by offering a holistic approach to data management and analytics.

Data pipelines can be divided into multiple types based on their primary processing mode and data handling approach—either real-time or batch processing, and streaming or non-streaming. They’re incredibly versatile, as the different operations in the pipeline can be exchanged, tweaked, or reordered to suit the needs of different departments in the organization.

Typically they are made up on three main components:

• Data source. This is where the data is generated, creating the contents for the pipeline—it can vary from structured to unstructured data and, in some instances, dynamic, real-time data streams coming from Internet of Things (IoT) devices and social media feeds.
• Processing. This is the heart of the pipeline where operations are applied to ingested data. These operations encompass tasks like data cleaning to ensure purity, validation for accuracy, aggregation for coherence, or transformation to make it ready for analysis.
• Data destination. This is the final stop where data is moved to be stored. It can be anything from a high-performance database to a consolidated data warehouse or data lake, all of which can be optimized for big data analytics.

ETL vs. Data Pipeline: Key Differences

ETL and data pipelines do essentially the same type of work, but they come at it from different angles—here’s a closer look at where and how they diverge.

Purpose and Function

While both are used for data processing, their purposes and functions differ. ETL is most commonly used for batch processing thanks to its ability to handle large volumes of data and focus on data cleaning and preparation for analysis. Data pipelines, on the other hand, are more versatile and capable of handling both batch and real-time data processing streams.

Flexibility and Scalability

Data pipelines have an edge over ETL tools when it comes to flexibility and scalability. ETL processes are typically linear and follow a fixed sequence, while data pipelines can be simple and straightforward or be made more complex with multiple branching and merging paths. This makes them more adaptable to changing data environments.

Complexity and Implementation

ETL processes are generally much easier to implement than data pipelines, which range greatly in complexity due to their flexibility and versatility. However, it’s important to note that the complexity of each solution depends on the desired outcome of the processed data.

Transformation Process: ETL vs. Data Pipelines

The transformation process is pivotal in data processing and results in different outcomes in both ETL and data pipelines because of their unique approaches that cater to different needs.

ETL Transformation

The ETL process predominantly relies on a centralized data transformation process. As soon as the data is extracted from its source, it undergoes transformation before it gets loaded to its final destination. The transformation itself encompasses a variety of operations, depending on the data source and type.

For example, it might include data cleaning to fix anomalies and errors, data enrichment to fill in missing gaps, and aggregation to summarize specific data points. The primary objective behind the transformations in ETL solutions is to make raw data suitable for analysis, ensuring it’s clean, consistent, and seamlessly integrated.

Data Pipeline Transformation

Data pipelines adopt a more decentralized approach to data transformations, which allows data to undergo multiple transformations at different points throughout the pipeline. This decentralization allows pipelines to be more flexible and scalable, which is particularly beneficial with fluctuating rates of live data.

Depending on the intricate demands of the data processing task, the nature, sequence, and complexity of the transformations can be tailored accordingly. This ensures that data is processed in a way that is most congruent with the end-use requirements, whether that’s for analytics, machine learning, or other data-driven applications.

Examples of ETL vs. Data Pipeline

While both ETL and data pipelines can be used in a wide variety of circumstances, for certain applications one solution is preferred over the other.

ETL Examples

ETL tools are widely used in data migrations, particularly when an organization switches from a legacy platform to a more advanced one. The company likely has years of data stored across numerous databases and systems, and the main objective is to consolidate this data into a location more suitable for analysis and decision-making.

This is where ETL tools become indispensable, as they can pull data directly from the legacy systems and storage environments—including data that contains inconsistencies, redundancies, or errors. In the transformation process, the data is cleaned, standardized, and enriched to align with current data requirements. The ETL tool would then migrate the data into the new environment for use and analysis.

Data Pipeline Example

Data pipelines are often used by companies that need to process large amounts of live data rather than bulk process stored data—for example, in a streaming platform like Spotify or Netflix. Business models that rely on real-time user engagement need access to tools that handle continuous data streams.

Unlike an ETL, a data pipeline enables companies to continuously process and analyze large streams of data as it’s being produced, resulting in real-time analysis. If a user frequently listens to a particular genre of music or watches one type of show, the platform would be able to immediately recommend new content.

Tools and Technologies: Popular ETL and Data Pipeline Platforms

Neither ETL or data pipeline solutions are new—both have been around long enough for there to be a wide range of options available on the market. They range in specialty, complexity, and cost.

ETL Tools

Traditional ETL tools were used for data management before cloud computing and big data came along. They’ve evolved to keep pace with technology, making them indispensable for the modern business. Here’s a look at some of the most widely used:

• Informatica PowerCenter. A renowned ETL tool used by enterprises in data integration, Informatica PowerCenter is compatible with a variety of platforms and offers numerous rich features that ensure complex integration and data reliability across diverse ecosystems.
• IBM InfoSphere DataStage. Part of the IBM Information Platforms Solutions suite, this ETL tool allows users to seamlessly integrate data from myriad sources, making it particularly useful for organizations with vast data landscapes. It’s highly adaptable and compatible with many IBM solutions.
• Oracle Data Integrator (ODI). A holistic ETL platform that employs a graphical environment to build, manage, and maintain data flows, ODI’s versatile nature means it caters to both conventional and modern data environments.

Data Pipeline Tools

At a time defined by real-time insights and data flows, the traditional approach of batch processing often falls short—this is where data pipeline solutions come into play. Here are some of the most widely used:

• Apache Kafka. This free and open-source platform for building tailored, real-time data pipelines and applications is best at processing fast-moving, vast volumes of data in near real-time.
• Google Cloud Dataflow. This fully managed Apache Beam pipeline management service is a solid choice for acquiring low-latency insights, ensuring businesses are able to gain access without the typical lag associated with batch processing.
• Amazon Kinesis. Part of Amazon Web Services (AWS), this collection of data management services is adept at real-time data streaming and is able to process massive amounts of data from numerous sources simultaneously. This makes it indispensable for businesses operating globally.

Bottom Line: ETL vs. Data Pipeline

ETL and data pipelines are two different methods for data processing with distinct functionalities designed to meet different use cases. ETL tools take a more traditional approach that tends to be better suited for batch processing large volumes of data. Data pipelines are flexible and can handle real-time data processing and streams.

Choosing between them should depend on multiple factors, such as your specific needs and the nature of your data. When dealing with large volumes that need cleaning and standardization, ETL is the best option. However, real-time data processing from multiple sources can easily overwhelm ETL solutions, making data pipelines the ideal alternative.

As the data landscape continues to evolve, expect to see new developments in both ETL and data pipelines, making them even more efficient and effective at handling the ever-increasing volumes of data businesses rely upon for decision-making and competitive advantage.

To learn more about enterprise approaches to data management, read The Future of Data Management next.