Types of Data Integration: ETL, ELT, and More

Modern businesses rely on data from various sources, including customer interactions, online transactions, IoT devices, SaaS applications, and internal operations. To make sense of it all, companies need a way to bring these streams together into a unified view. That is the role of data integration.

Among the many approaches, two acronyms stand out: ETL and ELT. But these are not the only strategies available. In this article, we’ll explore the main types of data integration, explain how they work, and highlight when to use each one.

Data integration is the process of combining information from multiple sources into a single, consistent dataset that is ready for analysis, reporting, and advanced use cases, such as AI. The goal is to provide decision-makers with reliable, timely, and unified data, rather than fragmented or siloed information. Without integration, businesses risk making decisions based on incomplete or outdated insights.

Key Types of Data Integration

Regarding data integration, there is no single “best” method. Each approach was designed to address a specific set of challenges, and understanding these differences helps businesses select the one that best fits their goals. Let’s look at the most common strategies in more detail.

ETL (Extract, Transform, Load)

ETL has been the basis of data integration for many years. In this approach, data is first extracted from its sources, then transformed into the required format, and only after that is loaded into a destination system, such as a data warehouse.

Because the transformation happens before loading, companies have substantial control over data quality and structure. It makes ETL especially useful in industries where accuracy and compliance are crucial, such as banking, insurance, or healthcare. However, ETL processes can be slow and resource-heavy, which is why they are less suitable for handling today’s massive, real-time data streams.

ELT (Extract, Load, Transform)

ELT changes the traditional model. Here, data is extracted and immediately loaded into the target system, where it is then transformed using the computing power of that platform.

This method has gained popularity due to the rise of cloud data warehouses, including Snowflake, BigQuery, and Databricks. Instead of spending hours preparing data before loading, companies can ingest raw data quickly and decide later how to transform it. ELT offers flexibility and scalability, making it an ideal choice for modern businesses that require rapid access to up-to-date information.

CDC (Change Data Capture)

When you have a database with millions of records and several thousand of them change every hour, copying the entire dataset repeatedly would be wasteful. Change Data Capture (CDC) can help.

CDC tracks and replicates only the changes: new entries, updates, or deletions, rather than the full dataset. It enables systems to be kept synchronized in near real-time without overloading networks or storage. For companies in e-commerce, logistics, or financial services, CDC is invaluable because it ensures that dashboards, reports, and applications always reflect the latest data.

Data Virtualization

Not all data integration involves physically moving data. With data virtualization, information stays where it is, and users access it through a virtual “layer” that connects multiple systems in real time.

It is a single dashboard that gives you a unified view without the heavy lifting of migration. It is beautiful for organizations that cannot or do not want to duplicate large datasets due to cost or regulatory requirements. However, because performance depends on the speed of the source systems, virtualization works best for queries and reporting, not for deep analytics.

Application-based Integration

Finally, many companies integrate data directly at the application level. Using APIs, pre-built connectors, or a specialized platform, the systems can interact with each other without complex data pipelines.

This approach is ideal for SaaS-heavy environments where marketing, sales, and operations tools need to be connected quickly. It’s not always suited for large-scale, enterprise-wide integration, but for automating workflows and ensuring smooth data exchange between apps, it’s often the simplest and most cost-effective solution.

Choosing the Right Approach

There is no single integration method that fits every organization. The choice depends on factors like:

Data volume: ETL for smaller, structured datasets; ELT for big data.

Speed requirements: CDC for real-time data, ETL/ELT for batch processing.

Infrastructure: Cloud-native solutions often favor ELT, while legacy systems tend to stick with ETL.

Budget & complexity: Virtualization or application-based options may save time and cost.

Here’s a simple rule: 

• Use ETL when you need strict governance and structured, high-quality data.

• Use ELT when you want flexibility and scalability in the cloud.

• Use CDC when real-time is critical.

• Use virtualization or application-based integration when speed and accessibility matter more than heavy analytics.

Data integration is the foundation for meaningful analytics and AI. ETL, ELT, CDC, or other approaches, the right choice depends on your business goals and technical environment.

At DataEngi, we help companies design integration strategies that fit their unique needs, building scalable, cost-effective, and future-ready data ecosystems.

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