As businesses grow, so does their appetite for data. Data is a valuable asset for modern-day organizations. It plays a key role in decision-making, optimizing organizational workflows, and improving performance.

However, as data sources increase, it can be challenging to collect and integrate data effectively. ETL pipelines play a key role in organizing multiple data sources and streamlining data flows. Let’s explore ETL pipelines in-depth and discover why modern-day businesses need them.

What Is an ETL Pipeline?

ETL (Extraction, Transformation, Loading) data pipeline collects procedures that help organizations extract and process data. It is a process where data is extracted from a source, transformed, and loaded into a target destination for analysis and other purposes. Usually, this destination is another data mart, database, or data warehouse.

As the name suggests, the ETL process integrates data from various data sources by leveraging data transformation and warehousing. To understand what ETL pipelines are, we must understand their different phases and what happens in those phases.

In the first phase (Extraction), we extract data from diverse sources. Applications, databanks, sensors, and business systems are famous examples of these sources. Since we extract data from multiple heterogeneous sources in its raw form, it is in several different formats.

Therefore, we must move on to the next phase (Transformation), converting raw data using various transformation operations. We convert raw data into a format that allows us to use it in other platforms and applications.

However, we can’t access the converted data directly by applications. Therefore, we must move on to the final phase (Loading). This is where we make data accessible in a consistent format by loading it into a target ETL database or data warehouse. It can be used for visualization and reports.


An illustration of how data is collected through Source: TimeXtender

Use Cases of ETL Data Pipeline

ETL pipelines are usually used to create data flows that facilitate fast and timely decision-making. It is viable in several business scenarios where we centralize several data sources and provide organizations with a consolidated data view. Here are some of the ways ETL pipelines help businesses in the real world:

  • Facilitating data migration from legacy systems to the cloud or other new repositories.
  • Obtaining a consolidated view of data by centralizing all data sources to obtain a consolidated version of the data.
  • Integrating several data sources into one system. Accumulating data from a marketing automation platform to a CRM platform is one such example.
  • Creating a stable dataset for data analytics tools that are already transformed and structured allows organizations to benefit from real-time analytics.
  • Ensuring compliance with various standards such as CCPA, GDPR, and HIPAA, allowing companies to omit sensitive information before loading the converted data to a target system.

Data Pipeline vs. ETL Pipeline Definition

ETL and data pipelines are similar in some ways. However, there are significant differences between them. People mistakenly use both these terms interchangeably. While ETL and data pipelines both move data from one place to another, both serve a different purpose and application.

ETL involves a sequence of processes where we gather data from different sources, transform it, and load it into an output destination. On the other hand, a data pipeline is a more generalized version of ETL, where we transfer data between two or more systems. More accurately, ETL pipelines are a subset of data pipelines. However, in data pipelines, transforming or normalizing data isn’t necessary.

Why Modern-Day Businesses Need ETL Pipelines

An ETL pipeline is a foundation for business intelligence and data analytics. We must source data from several different systems (web reporting, CRMs, social media platforms, etc.) to provide valuable insights. However, before giving users any insight, we must move, consolidate, alter, and fit the data with the functions and parameters of the destination database.

Making data available for analysis and visualization is known as data warehousing. Any organization that relies on data warehousing needs an ETL pipeline to gather, read, and transfer large volumes of raw data. Without an efficient ETL pipeline, organizations can’t leverage diverse data sources across disparate platforms.

An ETL pipeline allows users to load data into a single data warehouse, data store, or database, enabling them to access information quickly. The procedure involves several transformations such as joining, sorting, merging, filtering, aggregating, and reformatting data. These operations help users extract critical insights from raw data and make it easier to interpret.

However, processed data is still difficult to interpret for non-technical users. Therefore, we need graphical illustrations and visualizations like dashboards to communicate key insights. With that said, dashboards and data charts are not part of the ETL process. Instead, they are a part of data warehousing, built on top of an ETL pipeline.

ETL pipelines allow us to break down data silos and consolidate data for analysis and insights. Ultimately, it enables business leaders to make timely and effective decisions by collecting data from various sources and analyzing it. In other words, an ETL pipeline is the foundation of analytics.

Benefits of Data Warehousing and ETL

We can’t overstate the importance of ETL to a business’s data warehousing efforts. An ETL pipeline can help organizations in the following ways:

Increased Query and System Performance

Data volumes are expanding rapidly. There is a growing need to prepare data for advanced machine learning and AI-driven processes with time. These processes give your organization a significant edge in the market, making way for optimization and growth. However, without clean and consistent data, machine learning is useless.

The absence of effective ETL pipelines severely limits an organization’s ability to leverage data effectively for predictive analysis and performance optimization. An ETL pipeline is a must for your organization if you want to make most of your analytical capabilities.

Timely Access to Data

Organizations collect information and data in various places and from several different sources. Data may come in PDF documents, Excel files, images, databases, JSON, XML, or other formats.

Moreover, this data is sourced from several different platforms and technologies, such as Oracle, MySQL Server, IBM Db2, Sybase, Amazon Redshift, MS Dynamics CRM, Salesforce, and SAP. Sourcing data from such a diverse number of sources can easily create bottlenecks in the data pipeline.

ETL pipelines allow you to accumulate data from various sources, giving users real-time access to information. Providing accurate data at the users’ fingertips allows them to leverage critical insights for timely decision-making.

High Return on Investment (ROI).

Today’s ETL pipelines are a great alternative to manual transformations and custom coding. They make it easier to perform critical transformations and adjust data flow based on business rules for more accurate and faster processing.

Enhanced Business Intelligence

Since ETL pipelines break down data silos, organizations gain access to previously unused data. As a result, companies can streamline the flow of information and give team members access to the data they need. You can create a single source of truth and give self-service capabilities to employees who can use the data most effectively.

ETL Pipeline: A Key Asset for Modern-Day Enterprises

ETL pipelines enable modern-day enterprises to leverage data effectively. To learn more about key technologies in modern-day enterprises, follow my blog.