ETL vs data integration: Understanding the differences

Data is often referred to as the oil of the 21st century. It helps organizations understand the business trajectory and gain actionable insights. However, before it can be used, data must be thoroughly collected and processed using techniques like ETL (Extract, Transform, Load) and data integration.

Data integration and ETL are techniques used to streamline data workflows within an organization. They help unify data, convert it to a usable form by applying necessary transformations and integrations, and improve its accessibility across the organization. Both techniques are related to one another but have subtle differences.

In this article, we’ll explore data integration and ETL in detail. We’ll break down what data integration and ETL really mean, and why data movement, transformation, and unification at scale are crucial for modern businesses.

What is data integration?

Most modern organizations use multiple platforms and services for various purposes, such as marketing, advertising, email, and customer interactions. While each of these services helps improve business efficiency and growth, this architecture means that all generated data is stored in a scattered and decentralized manner.

This scattered storage bottlenecks data-related tasks and workflows such as analytics, data science, and reporting. The data integration process aims to collect data from these touchpoints and store it in a different format for improved accessibility. It involves building robust pipelines that collect and combine data and is usually a part of operations like ETL and ELT.

It involves the following key steps:

• Source identification: Identifying and listing all available data sources. These can be relational database systems (RDBMSs), CSV files, or cloud storage for unstructured files.
• Data standardization: Applying necessary transformations to standardize the data format.
• Pipeline formation: Designing and optimizing pipeline architecture to collect and integrate the data in a single location.

Data integration activity provides a holistic view of the entire organization's operations. It removes data silos by allowing users to access data from any domain or workflow and improves process efficiency and time-to-market.

Moreover, a data integration solution design can take multiple approaches. For example, the design may involve a complete migration, during which the entire data is physically moved to a new centralized location. Or, it may include virtualization, which involves accessing data from its original location using API endpoints. Virtualization allows users to access data from a single interface without any data movement.

Benefits of data integration

Having a holistic view of your entire data estate has several benefits, such as:

• Breaking down silos: Integrations improves data accessibility across the entire organization. It provides users with a single interface to access data from multiple sources, eliminating unnecessary inter-team dependencies.
• Improved efficiency for data-related tasks: Teams can build dedicated pipelines with specific transformations to receive data in a set format. This saves time during data analysis for data science and analytics projects. Moreover, since teams can access any data they want, they are no longer delayed by dependencies on other departments, allowing for faster experimentation and insights.
• Streamlined reporting and analytics: Integrated data ensures consistency across reports and dashboards. Teams no longer have to manually compile information from various sources, which reduces the risk of errors and improves the reliability of business intelligence outputs.
• Enables data-driven innovation: With unified access to diverse datasets, organizations can identify patterns, generate new ideas, and deploy AI and machine learning (ML) models more effectively. This fosters innovation and supports smarter decision-making across the business.

What is ETL?

ETL stands for Extract, Transform, Load, and is a standard data migration process used across the industry.

It consists of three main steps:

• Extract: The extraction step involves collecting data from one or multiple sources. These sources can include relational databases hosted on different platforms, CSV or text files stored in various cloud storage, or JSON responses from APIs.
• Transform: Once the data sources are connected, various transformations are implemented as an intermediate step. These transformations help standardize the schema, clean the data (removing duplicates, handling null values, etc.), and create new information or views through aggregation operations.
• Load: Once the transformations are complete, the data is loaded to a centralized location, such as a data warehouse, data lake, or lakehouse. Depending on the access settings, the clean and transformed data is accessible to all teams from a single endpoint.

ELT: The modern ETL

While ETL has been a popular data integration technique for some time, ELT has gained popularity as an alternative in recent years. It involves the same steps as ETL, but shifts the order by applying data loading before transformation, hence the name “Extract, Load, and Transform.”

As data needs grow, ELT has been recognized as the superior alternative, offering various benefits over its counterpart, including:

• Better flexibility: Since the destination location contains raw data, data analysts and scientists can transform it according to their needs. The data can be modified iteratively for each use case, and no special changes are required to the ELT pipeline. This flexibility allows for quick adaptation to changing business requirements, ensuring teams always have access to the most up-to-date data. It also reduces the risk that the original data will be lost in transformation - a common and frustrating problem with the ETL process.
• Faster data loading: By offloading complex transformations to the data warehouse, the data loading pipeline becomes more efficient, allowing teams to access the latest data more quickly.
• Increased process efficiency: Leveraging the power of modern data warehouses for transformations reduces the processing burden on upstream systems. This streamlines the overall data workflow, minimizes resource contention, and enables engineering teams to focus on higher-value tasks instead of maintaining complex ETL logic.

ELT is quickly becoming the industry standard for data integration. Although this article focuses more on ETL, everything we have discussed applies equally to both procedures.

ETL real-world use case

To make this more concrete, let’s look at the data integration process using a real-world use case.

Take an e-commerce store. Such stores often consist of multiple modules, including a customer chatbot, an inventory management system, a social media profile, and an email marketing system. Each module is often implemented using different platforms and generates data stored across various locations. These diverse data sources hold valuable insights for business reporting and data science initiatives.

However, accessing and consolidating data across platforms can be challenging and requires close coordination between teams. For instance, if the data science team needs customer reviews from social media, they must request access and formatting support from the social media team. This leads to delays and inefficiencies.

An ETL pipeline streamlines this by collecting and storing all relevant data in a centralized location. It connects to each source system, extracts the data, applies necessary transformations, and loads it into a unified platform. This not only ensures consistency and accessibility but also empowers teams to make data-driven decisions faster without constantly relying on cross-team coordination.

ETL and data integration: head-to-head

Let’s do a one-on-one comparison between ETL and data integration.

• Data integration:
  • A specific data transformation technique.
  • Focuses on data unification and availability, making data usable for a specific use case.
  • Depending on the business requirement, the integration process can be real-time, a batch job, or a combination of both.
• ETL:
  • ETL is a superset of data integration that covers the entire process of transforming data for a specified use case.
  • Gathers data from one or more sources, transforming it according to business logic, and delivering it to an end system.
  • It covers three main steps: Data extraction, transformation (which includes data integration), and loading.
  • Mostly a batch process. ETL jobs are often scheduled outside work hours, so the integration completes before the start of the business day, ensuring fresh data is available for reporting and analysis.

Breaking silos - Unlock your data-driven potential

Data can be a game changer for businesses, but it requires complex processing. Managing transformations effectively is crucial to unlocking data’s true potential. That’s where dbt comes in—a modern data transformation tool that helps bring structure, consistency, and control to your pipelines.

If you’re embarking on a journey to streamline your data operations, dbt is the control plane you need to unify data transformation efforts across your enterprise. dbt enables you to modernize your data pipelines with features like version control, modular SQL, testing frameworks, and automated data deployment pipelines that bring a DevOps-like rigor to analytics workflows.

With dbt, transformations become reproducible and trustworthy, allowing you to shift from reactive data cleaning to proactive data modeling. It supports scalable, governed development by making it easy to track changes, implement peer reviews, and ensure data quality at every step.

Beyond just transformations, dbt assists with data integration by allowing teams to write reusable code blocks for common operations, such as standardizing data formats across sources. This makes your pipelines cleaner, more consistent, and easier to maintain over time.

Whether you're an engineer, analyst, or business user, dbt empowers you to own your part of the data journey with a unified toolkit. Ready to see the difference? Book a demo today.