Businesses have a wealth of data at their disposal, but it’s often spread among different systems. This scenario makes it challenging to get a clear picture of what’s happening in the business.
SEE: Here’s what employers are looking for in an ETL and data warehouse developer.
That’s where data integration and ETL 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 life cycle.
Jump to:
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. However, the overall goal is to combine data from multiple sources to provide businesses with a 360-degree view of the information, whether it be customer data from several social media sites or research findings from a scientific study.
For data integration to be successful, it’s crucial to understand what data is needed and where it’s stored. Once this information has been gathered, the next step is determining how the various datasets can be brought together. This may involve using ETL tools or manual processes such as manual data entry or CSV file importation.
Data integration finds its application in a variety of scenarios, each presenting unique requirements and challenges:
Large organizations often grapple with data siloed across different departments. Each department may operate its own systems and databases, leading to fragmented and inconsistent data.
In such a scenario, data integration comes to the rescue, merging this data to provide a comprehensive, unified view of the company’s operations. Enterprises must address data heterogeneity and ensure data consistency across the organization.
In the healthcare sector, providers often need to integrate patient data from various sources such as electronic health records, lab systems and insurance databases. This integration provides a complete patient history, which is crucial for improving diagnosis and treatment. This involves handling sensitive data, ensuring data privacy and maintaining compliance with regulations like HIPAA.
Financial institutions often integrate data from various internal and external sources to provide a complete financial overview for a client. This could include data from checking and savings accounts, credit cards, loans and investment accounts. Institutions must manage complex financial data and ensure data accuracy and security.
Brands often integrate data from different social media platforms to gain a comprehensive view of their online presence and customer sentiment. This use case involves handling unstructured data and large volumes of data.
To further illustrate the concept of data integration, let us consider some specific examples:
ETL — extract, transform and load — is one of the simpler forms of data integration. It’s a three-step process used to collect data from multiple sources, such as enterprise resource planning systems, e-commerce platforms, legacy systems, customer relationship management 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.
ETL, a specific form of data integration, is used in various scenarios, including:
One of the most common use cases for ETL is loading data from various sources into a data warehouse for business intelligence purposes. This involves the extraction of data from source systems, transformation into a consistent format and loading into the data warehouse.
ETL can be used to migrate data from one system to another, such as when replacing a legacy system. Data is extracted from the old system, transforming it to match the schema of the new system and loading it into the new system.
When a company acquires or merges with another company, ETL is often used to integrate data from the different systems of the two companies. The process involves extracting data from both systems, transforming it into a consistent format and loading it into a new or existing system.
To illustrate the concept of ETL, let’s look at some specific examples:
From the use cases and examples presented above, it’s evident that 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.
SEE: Explore the differences between data migration and data integration.
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 whether ETL will be the most useful form of data integration.
The main difference between data integration and ETL is that data integration is a broader process. It can be used for more than moving data from one system to another. It often includes:
To better illustrate the differences between ETL and data integration, let’s look at a 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 nonalcoholic category of an even larger food and drinks sales category. On the other hand, the subsidiary may lump Red Bull sales into a broad nonalcoholic 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, especially when business rules to determine valid transformations aren’t laid out well.
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.
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 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’s 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 and data warehouse developers and other data professionals on staff who can use data integration and ETL tools effectively.
