If you are considering using a data integration platform to build your ETL process, you may be confused by the terms data integration versus ETL. Here’s what you need to know about these two processes.
Companies have a wealth of data at their disposal, but it is often spread over different systems. This scenario makes it challenging to get a clear picture of what is happening in the business.
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That’s where data integration and ETL — or Extract, Transform and Load — come in handy to support greater data visibility and usability. While these two concepts are closely related, data integration and ETL have different goals in the data management lifecycle.
What is data integration?
Data integration is the process of giving users a unified view of data coming 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 into a commercial application that provides business users with a 360-degree view of the customer.
- The research results from many sources can be combined into one unit in a scientific application, such as a bioinformatics study.
For a successful data integration, it is crucial to understand what data is needed and where it is stored. Once this information has been collected, the next step is to determine how to bring the different data sets together. This may involve using ETL tools or manual processes, such as manual data entry or importing CSV files.
What is ETL?
ETL is one of the simpler forms of data integration. It is a three-step process 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 comparable?
Data integration and ETL are closely related concepts. In fact, ETL can be seen 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 is important to note that not all data integration solutions use ETL tools or concepts. In some cases, it is 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 whether 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 that data is accurate, complete and timely.
- To define master reference data: Create 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 multiple classifications for goods and consumers to separate marketing campaigns.
A subsidiary of the same company could achieve this with a simple product hierarchy and customer classification scheme. In this case, 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 beverage sales category. On the other hand, the subsidiary can place Red Bull sales in a broad non-alcoholic beverage class without further differentiation, as it offers only a handful of different product types.
While this example illustrates how data integration can provide greater clarity for business decisions, it also demonstrates how data quality is essential for effective data integration. Without clean and well-organized data, companies risk making decisions based on incomplete or incorrect information.
ETL was an early attempt to deal with such issues, but the transformation step can be problematic because the business rules to determine valid transformations are not well established.
There should be clear rules governing how certain data should be aggregated. 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,” while another uses just the letter “f.” Data integration tools and technologies have been developed to help you with such problems.
The future of data integration, ETL and ELT
In the past, data integration was mainly done using ETL tools. But in recent years, the rise of big data has led to a shift to ELT – extract, load, and transform tools. ELT is a shorter, more analyst-centric workflow that can be implemented using scalable multicloud data integration solutions.
These solutions have clear advantages over ETL tools. Third-party providers can produce common unpacking and loading 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 organization.
In the age of big data, data integration needs to be scalable and multicloud compatible. Managed services are also becoming the standard for data integration, as they provide the flexibility and scalability organizations need to keep up with the changing use cases of big data. 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.