The widespread use of computer devices and information systems forms a significant amount of data across domains. Such electronic database stores varieties of user details and business operations. And, data analysis helps to use this data for developing a business intelligence framework. That is where data integration and ETL come in. These both help to handle a large amount of data from various sources and make better data-driven business decisions.

While data integration and ETL are connected firmly, they differ in multiple ways. Here, we will drill down in what exactly they are, and what are their differences.

Data Integration vs. ETL

In the modern world of advanced data marketing; ETL, and data integration, both techniques are equally important. Data integration consists of processes such as ETL, ELT, and data federation while ETL is a three-step process, which takes place when you want to transfer specific information from one source to the other. 

What is Data Integration?

In the data integration process, the data stored at multiple internal and external sources are combined to offer a unified view to the users. Such sources include customer feedbacks, market figures, demographics, and more.  

The data integration process varies from one application to the other. You can combine the results from different repositories in a single project or can even merge the databases of two organizations, depending on the need.

What is ETL?

ETL is a part of data integration. In the ETL process, users extract, transform, and load data in a data warehouse environment. It occurs before the data is stored and unified.

ETL process starts with extracting the data from different sources. A data analyst or data scientist then transforms the full or partial data as per the need after converting it to the required format. He then removes duplicates, organizes the data, and loads it into the data warehouse.

How to Use ETL

You need to integrate data sources with careful planning and testing while building an ETL infrastructure. For this process, you can take help from either a data expert or a dedicated IT department or can use an ETL software.

Steps involved in an ETL process:

1. Gather useful data from various reliable sources
2. Process the data as per the requirement using an ETL tool
3. Use the storage space of ETL tool if available or create a secure warehouse to store the data
4. Ensure the information is transformed and stored properly

Once you get comfortable with the use of ETL tools, you can successfully synchronize your data organization and analysis processes.

How to Use Data Integration

When the data is cleaned and stored, you can perform the post-ETL data integration process. Using a full data integration process, you can:

1. Filter the data based on the patterns across aspects that you wish to analyze 
2. Convert it into the form such as a scatter diagram, heat map, or anything else as per your choice
3. Recover data from the warehouse and upload it into analytics, visualization, or report generation tools
4. Convert the data into actionable insights

Besides their working and uses, the data integration and ETL differ in a few other ways.

• Data integration does not involve the data transfer process, while the ETL consists of moving information from one system to another.
• Generally, commercial and scientific applications use the data integration process. On the other hand, most of the data warehousing techniques contain the use of the ETL process.

Conclusion

According to an IDC (International Data Center) prediction, the Global Data sphere will grow to 175 Zettabytes by 2025, which was 33 Zettabytes in 2018. To satisfy this substantial data demands, expertise with integrating data using Talend like ETL tools will help to create a secure, flexible, and sustainable warehouse environment.

A right understanding of the differences between data integration and ETL systems will help you to make the right choice with your data and get the success you want. As part of this, an experienced technology service provider can help to get the best combination of these systems whenever possible. It will help you to get complete data integration even with little or no experience of working with data.