If you’re considering using a data integration platform to build your ETL process, you may be confused by the terms data integration vs. ETL. Here’s what you need to know about these two processes.
Businesses have a wealth of data at their disposal, but it is often spread out among different systems. This scenario makes it challenging to get a clear picture of what’s happening in the business.
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That’s where data integration and ETL — or Extract, Transform and Load — come in to support greater data visibility and usability. Although these two concepts are closely related, data integration and ETL serve distinct purposes in the data management lifecycle.
What is data integration?
Data integration is the process of providing users with a unified view of data that comes from multiple disparate sources. It follows different processes depending on the application. For example:
- A company can merge customer information from its Facebook, Twitter and Instagram social media databases in a commercial application that provides business users with a 360-degree view of the customer.
- The research findings from numerous sources may be combined into a single unit in a scientific application, such as a bioinformatics study.
For data integration to be successful, it is crucial to understand what data is needed and where it is stored. Once this information has been gathered, the next step is determining how the various data sets can be brought together. This may involve using ETL tools or manual processes such as manual data entry or CSV file importation.
What is ETL?
ETL is one of the simpler forms of data integration. It is a three-step process that is used to collect data from multiple sources, such as ERP systems, e-commerce platforms, legacy systems, CRM systems and other data sources. From these sources, ETL converts data into a format that a central system can use and then loads it into a data warehouse.
How are data integration and ETL similar?
Data integration and ETL are closely related concepts. In fact, ETL can be thought of as a subset of data integration. This is because both processes involve combining data from multiple sources into a single repository.
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However, it’s important to note that not all data integration solutions use ETL tools or concepts. In some cases, it’s possible to use alternative methods such as data replication, data virtualization, application programming interfaces or web services to combine data from multiple sources. It all depends on the specific needs of the organization if ETL will be the most useful form of data integration or not.
How are data integration and ETL different?
The main difference between data integration and ETL is that data integration is a broader process. It can be used for more than just moving data from one system to another. It often includes:
- Data quality: Ensuring the data is accurate, complete and timely.
- Defining master reference data: Creating a single source of truth for things like product names and codes and customer IDs. This gives context to business transactions.
ETL and data integration in action
Let’s look at one scenario: A large food and beverage conglomerate may need numerous classifications for goods and consumers to separate marketing campaigns.
A subsidiary of the same company might accomplish this with a simple product hierarchy and customer classification scheme. In this circumstance, the conglomerate may label a can of Red Bull as an energy drink, a beverage that is part of a non-alcoholic category of an even larger food and drinks sales category. On the other hand, the subsidiary may lump Red Bull sales into a broad non-alcoholic beverage class without further differentiation, because it only offers a handful of different product types.
While this example illustrates how data integration can provide greater clarity for business decisions, it also shows how data quality is essential for data integration to be effective. Without clean and well-organized data, businesses run the risk of making decisions based on incomplete or incorrect information.
ETL was an early attempt to deal with such problems, but the transformation step can be problematic, where business rules to determine valid transformations are not well laid out.
There must be clear rules defining how to aggregate certain data — examples include documenting sales transactions or mapping database fields where different words are used to describe the same field. For example, one database uses the word “female,” whereas another simply uses the letter “f.” Data integration tools and technologies were developed to help with such issues.
The future of data integration, ETL and ELT
In the past, data integration was primarily done using ETL tools. But, in recent years, the rise of big data has led to a shift towards ELT — extract, load and transform tools. ELT is a shorter workflow that is more analyst-centric and that can be implemented using scalable, multicloud data integration solutions.
These solutions have distinct advantages over ETL tools. Third-party providers can produce general extract-and-load solutions for all users; data engineers are relieved of time-consuming, complicated and problematic projects; and when you combine ETL with other cloud-based business applications, there is broader access to common analytics sets across the entire organization.
In the age of big data, data integration needs to be scalable and compatible with multicloud. Managed services are also becoming the standard for data integration, because they provide the flexibility and scalability that organizations need to keep up with changing big data use cases. Regardless of how you approach your data integration strategy, make sure you have capable ETL/data warehouse developers and other data professionals on staff who can use data integration and ETL tools effectively.
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