Data Integration vs. ETL: Beyond Buzzwords - What You Need

April 3, 2024
15 min read

Data is the lifeblood of many organizations, but it often resides in scattered sources like databases, applications, and cloud storage systems. This makes it difficult to get a holistic view of your data and extract meaningful insights. To win this battle against this mess, data integration and ETL are your secret weapons. 

Data integration is the umbrella term for the entire process of integrating data from multiple sources into a suitable format. It ensures a consistent view of your data, regardless of where it originally came from. On the other hand, ETL is a specific type of data integration focused on populating data warehouses and data marts for analysis. 

While both are essential for managing data, they have key differences in scope and purpose. This article helps you understand the key differences between data integration vs. ETL and choose the right approach for streamlining your data workflows. 

What is Data Integration?

Data integration is the process of combining data from various sources into a unified and coherent format. This gives you a simplified view of data, which can be used by downstream applications for analysis, reporting, and decision-making. Data integration can be accomplished by implementing various methods. 

Common data integration techniques include ELT and ETL.

Manual Data Integration 

Manual data integration refers to integrating data from multiple sources, such as databases, spreadsheets, or files, by manually extracting, transforming, and loading data into a single destination. This approach is suitable for one-off migrations and smaller datasets. For enormous datasets, it can be a labor-intensive and error-prone method. The limitations of manual integration processes can be streamlined and automated using data integration tools

Application Integration 

Application integration enables synchronization between distinct software applications and systems within your organization to facilitate seamless data exchange, communication, and collaboration. It involves creating a unified ecosystem where disparate applications can work together efficiently, sharing real-time information to support your business processes and decision-making. 

Batch Data Integration 

Batch data integration lets you integrate data from different sources in discrete batches rather than real-time or near-real-time. This approach allows to collect, process, and transfer data in predefined hourly, daily, or weekly intervals. Bach data integration is commonly used in scenarios where real-time processing is not required or feasible, such as periodic reporting or bulk data migration. 

Data Virtualization 

Data virtualization is a method for accessing and querying disparate data sources, such as databases, applications, & cloud-based services, as if they were all stored in one centralized location. Instead of physically moving or copying data from various sources into a central repository, data virtualization creates a virtual layer that summarizes the underlying data sources. This virtual layer provides a harmonious data view, enabling you to access and analyze it without knowing its physical location or structure. 

What is ETL?

ETL, which stands for extract, transform, load, is a long-standing data integration process that combines data from various sources into a consistent dataset for loading into a data warehouse, data lake, or other target system. It provides a foundation for data analytics and machine learning workstreams. Through a series of business rules, ETL enables you to cleanse and organize data to address specific business intelligence needs, like tailoring data to meet the specific requirements of ML models. 

The ETL process has three steps, which are listed below: 


Data extraction refers to the first stage of the data processing pipeline, where the raw (structured or unstructured) data is copied or extracted from the source location. The sources include databases (relational, NoSQL, etc), flat files (CSV or Excel), social media feeds, APIs, and more. This phase focuses on acquiring the necessary data from various sources and preparing it for further processing. 


After data extraction, the unprocessed data goes through various transformation processes according to its specific goal. This can involve the following methods: 

  • Using statistical methods or other strategies to fill in the missing data values. 
  • Identifying and fixing typos, outliers, and other data inconsistencies. 
  • Determining and eliminating duplicate records in the dataset. 
  • Ensure consistency in how data is represented (e.g., date formats, units of measurement).


Data loading, the final stage of the ETL process, involves transferring the transformed data from the staging area to its final destination. The target location can be databases, lakes, warehouses, etc. 

Data Integration vs ETL: Major Comparison

The key differences between data integration and ETL are as follows:

Features Data Integration ETL
Scope Broader scope of combining data from various sources. Specific to data warehousing workflows.
Activities Includes data extraction, transformation, loading, quality, replication, distribution, virtualization, and management. Focuses on extracting, transforming, and loading data.
Target System Can deliver data to various destinations (databases, data lakes, applications). Primarily targets data warehouses and data marts.
Data Volume Handles small to large data volumes. Typically handles high data volumes.

ETL vs Data Integration: Tools

Data integration and ETL are your allies in the fight to organize and utilize the ever-growing sea of information within your organization. While both serve the cause of data processing, they operate in different capacities. Here is a breakdown of popular tools for data integration and ETL.



Airbyte is a robust and popular data integration platform that caters to various data integration needs. It offers various methods—UI, API, PyAirbyte, and Terraform Provider—to build and manage data pipelines. By offering these options, Airbyte ensures there’s a method that suits different user preferences and technical backgrounds, making data integration accessible. 

Airbyte allows you to extract data from multiple sources and load it into a destination of your choice. It provides 350+ pre-built connectors that support structured, semi-structured, and unstructured data types. Additionally, you can integrate Airbyte with dbt, a robust data transformation tool, for complex transformations. This integration empowers you to manipulate and process your data efficiently through dbt models. 

Here are some of the amazing features of Airbyte:

  • If you can’t find the required source in Airbyte’s extensive connector library, you can build a custom connector using its Connector Development Kit within minutes. 
  • Airbyte supports CDC, which allows you to efficiently capture and synchronize only the changes made to the data from source to destination, helping minimize data transfer and improving efficiency, especially for large, constantly evolving datasets.
  • It provides an open-source Python library, PyAirbyte, enabling you to handle your data pipelines programmatically. It facilitates seamless and accurate data integration, allowing you to extract data from connectors supported by Airbyte and transform it with Python programming.

Oracle Data Integrator 

Oracle Data Integrator

Oracle Data Integrator (ODI) is a reliable data integration platform that caters to most styles of data integration—data-based, event-based, and service-based. ODI breaks down data silos by facilitating efficient movement, transformation, and unification of enormous data volumes. With its CDC capability, you can ensure your data in the target system stays up-to-date. It also provides robust data integrity control features, assuring the consistency and correctness of data. 

Microsoft SQL Server Integration Services (SSIS)

Microsoft SQL Server Integraton Service

SSIS is a robust platform for enterprise-level data integration and transformation tasks. It is equipped with connectors tailored for extracting data from various sources, including XML files, flat files, and relational databases. SSIS empowers practitioners to effortlessly build data flows and transformations using its graphical user interface. Additionally, SSIS offers extensive documentation to guide you in constructing custom workflows effectively.

AWS Glue

AWS Glue

AWS Glue is a serverless solution offered by Amazon that helps you run your ETL jobs without managing servers or infrastructures. To simplify data integration tasks, it supports 70+ diverse sources, such as Amazon Aurora and Amazon RDS for MySQL, PostgreSQL, and more. In addition to data integration, AWS Glue also helps you manage your data in a centralized catalog.



Informatica PowerCenter is a robust solution for implementing ETL processes. With PowerCenter, you can extract data from various sources, transform it according to the business logic, and load it into centralized repositories, like data warehouses. In addition to ETL functionalities, PowerCenter offers the capability to manage and explore metadata from various metadata repositories. This metadata management helps you understand and analyze your enterprise information better. 

Final Words

Data integration and ETL are potent processes for unlocking the potential of your organization's data. Data integration is the grand architect, providing a unified view of your information across disparate sources. Whereas ETL, the specialized engineer within this ecosystem, prepares and delivers data for specific purposes, often for in-depth analysis in data warehouses. While their functionalities differ, both are crucial for establishing a data-driven culture within your organization. By understanding these distinctions between data integration vs ETL and selecting the most suitable tool for your needs, you can move your data from a scattered collection into a strategic asset.

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