Information integration vs ETL: What are the differences?


If you’re thinking about utilizing an information integration platform to build your ETL procedure, you may be puzzled by the terms information integration vs. ETL. Here’s what you need to understand about these two procedures.

Cloud technology connected all devices with server and storage in datacenter background. Image: canjoena/Adobe Stock Companies have a wealth of data at their disposal, however it is typically spread out among various systems. This circumstance makes it challenging to get a clear photo of what’s taking place in the business.

SEE: Task description: ETL/data warehouse developer (TechRepublic Premium)

That’s where information combination and ETL– or Extract, Change and Load– come in to support higher information presence and use. Although these two concepts are carefully associated, information combination and ETL serve distinct functions in the data management lifecycle.

Dive to:

What is data integration?

Data combination is the process of offering users with a combined view of data that comes from several diverse sources. It follows various processes depending on the application. For example:

  • A business can merge customer details from its Facebook, Twitter and Instagram social networks databases in an industrial application that supplies organization users with a 360-degree view of the client.
  • The research findings from various sources might be combined into a single system in a scientific application, such as a bioinformatics research study.

For information combination to be effective, it is essential to comprehend what information is needed and where it is stored. When this information has been collected, the next action is identifying how the numerous information sets can be combined. This may include utilizing ETL tools or manual processes such as manual data entry or CSV file importation.

What is ETL?

ETL is among the simpler kinds of information integration. It is a three-step process that is utilized to gather data from numerous sources, such as ERP systems, e-commerce platforms, legacy systems, CRM systems and other data sources. From these sources, ETL transforms information into a format that a central system can utilize and then loads it into a data warehouse.

How are information combination and ETL comparable?

Data combination and ETL are closely related principles. In truth, ETL can be thought of as a subset of data integration. This is because both procedures include combining data from multiple sources into a single repository.

SEE: Data migration vs data integration: What’s the difference? (TechRepublic)

Nevertheless, it’s important to note that not all data integration solutions use ETL tools or ideas. Sometimes, it’s possible to use alternative techniques such as data replication, information virtualization, application programming interfaces or web services to integrate data from multiple sources. Everything depends on the specific requirements of the organization if ETL will be the most useful kind of data integration or not.

How are information integration and ETL various?

The primary difference between data combination and ETL is that data integration is a wider process. It can be used for more than simply moving information from one system to another. It often includes:

  • Information quality: Guaranteeing the information is accurate, complete and prompt.
  • Specifying master reference data: Developing a single source of reality for things like item names and codes and client IDs. This provides context to organization transactions.

ETL and data combination in action

Let’s take a look at one scenario: A large food and beverage conglomerate might need many categories for items and consumers to separate marketing projects.

A subsidiary of the very same company might accomplish this with a simple product hierarchy and client classification plan. In this situation, the corporation may label a can of Red Bull as an energy drink, a drink that belongs to a non-alcoholic category of an even larger food and drinks sales classification. On the other hand, the subsidiary may lump Red Bull sales into a broad non-alcoholic beverage class without additional differentiation, since it just provides a handful of various item types.

Must-read big data protection

While this example shows how information integration can provide higher clearness for service decisions, it also demonstrates how information quality is important for information combination to be reliable. Without clean and efficient data, services run the risk of making choices based upon insufficient or inaccurate details.

ETL was an early effort to handle such problems, however the improvement action can be problematic, where service guidelines to determine legitimate changes are not well set out.

There should be clear guidelines specifying how to aggregate certain data– examples include documenting sales transactions or mapping database fields where different words are used to explain the same field. For example, one database utilizes the word “female,” whereas another just uses the letter “f.” Data integration tools and innovations were developed to assist with such concerns.

The future of data combination, ETL and ELT

In the past, data combination was mainly done using ETL tools. But, over the last few years, the rise of huge information has actually resulted in a shift towards ELT– extract, load and change tools. ELT is a shorter workflow that is more analyst-centric and that can be executed utilizing scalable, multicloud information integration services.

These solutions have distinct benefits over ETL tools. Third-party suppliers can produce general extract-and-load solutions for all users; information engineers are eliminated of lengthy, complicated and troublesome tasks; and when you integrate ETL with other cloud-based company applications, there is wider access to typical analytics sets throughout the entire company.

In the age of huge data, data integration needs to be scalable and compatible with multicloud. Managed services are also becoming the standard for information combination, due to the fact that they provide the versatility and scalability that organizations require to keep up with altering big information use cases. Regardless of how you approach your information integration strategy, make certain you have capable ETL/data storage facility developers and other data experts on personnel who can utilize information combination and ETL tools efficiently.


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