Data Integration vs. ETL: Key Differences, Side-by-Side Comparisons, & Use Cases

The realm of data management is vast. Concepts can intertwine, and one can easily confuse primary functions. Data integration and ETL (Extract, Transform, Load) are some of the most crucial data-management methods.

Each involves distinct properties that help bring data from multiple sources together. Let’s dig deeper into ETL vs. data integration and explore the essence of the two concepts. 

What Is Data Integration?

In essence, data integration stands for the operation of collecting and merging multiple-source data into one place. The process translates to accumulating data from different outlets, cleaning it, and transforming it into readable, usable, and manageable data stored in a main repository. Business requirements like making decisions, data analysis, creating reports, and more, can benefit from data integration. 

Example Use Cases for Data Integration

In general, data integration is utilized when the need to merge data from different sources arises. For instance, you can use this method to integrate data and create reports that offer insight into an organization or a business. 

Data integration is perfect for tracking performance, pinpointing trends, etc. Here are some common use cases of data integration:

• Analysis: Organizations and businesses can use data integration to analyze differently-structured data from many outlets. This approach is perfect for performing market research, predictive analytics, etc.
• Data warehousing: Data integration is also commonly used to build and maintain a data warehouse. In other words, data integration means compiling data into a central repository with the goal of analyzing and reporting. 
• Decision-making: Having the right information at hand can help make the right decision. Data integration helps businesses and organizations with allocation, strategic planning, and product development, among other instances. In essence, those who use data integration can reach the best business decisions based on facts. 

Types of Data Integration

Data integration encompasses a wide range of activities. It can be divided into several types, which we’ve detailed below.

• Data Consolidation 

Consolidating data means combining and storing different data in a single place. Data consolidation allows the user to navigate different amounts of data from a single access point and transform raw data into readable information.

• Data Federation 

Data federation is a method used to query data from distinct outlets for digital rendition. It enhances the process of data integration. 

• Data Propagation 

Generally, data propagation means allocating data from one or multiple source data warehouses to a preferred database with local access. 

• Extract, Transform, Load (ETL)

ETL is a specific type of data integration. This process involves extracting, transforming, and loading data from numerous outlets into a designated data warehouse or another data repository. 

What Is ETL?

As mentioned, ETL is a tool that extracts, transforms, and loads data into data repositories. ETL is vital to the success of reporting, analytics, and as of recently, machine learning processes. Today, AI also relies on ETL tools.

Example Use Cases for ETL

Common ETL uses include the following:

• Data warehousing: ETL is frequently used to maintain and populate repositories. An ETL tool extracts data from sources, transforms it into a suitable format, and loads it into the data warehouse.
• Data migration: ETL tools are perfect for mitigating data between data systems. The best uses are for cloud and on-site data systems.
• Business intelligence: Commonly, ETL is used as an inseparable part of business intelligence to extract data from multiple data outlets, transform data into a format that works for data analysis, and load new data into a dashboard or other reporting asset.

Types of ETL

The ETL process can be distinguished based on several types.

• Enterprise

In general, these are ETL tools created by commercial organizations. Enterprise ETL tools are the most advanced and powerful data extraction tools today.

• Open-Source

Most open-source ETL tools are free to use and are excellent add-ons for sharing data and staying on top of the flow of information. 

• Cloud-Based

Cloud-based ETL tools come with great efficiency and offer flexibility in storing readily available data for users from anywhere. 

• Custom

Custom ETL tools are mainly used by major businesses with massive development budgets. The main perk of such ETL tools is the uniqueness and flexibility of extracted, transformed, and loaded data.  

Data Integration vs. ETL: 3 Key Differences

By placing data integration vs. ETL side by side, you can see they have some similarities. However, they are not the same. Below, we’ve made a table comparing data integration tools vs. ETL, showing the 3 points in which these methods differ.

Choosing The Right Method for Your Customer Data

Gathering data for analytics and segmentation is no easy process. To ensure you’ve found the right data collection method, you need to bear in mind the sort of information you’re looking to collect. After that, see how fast you need it and other aspects. 

Trust Rivery to Help Your Business Navigate Complex Data Mazes

As the leader in data-management tools, data orchestration, dataOps management, and everything data, Rivery can help you tackle all data challenges. Our team of dedicated professionals knows how to identify your business needs and offer the right solution.

Reach out to us today and try out our services, free of charge, for 14 days!