Data integration, data migration, or ETL – which do you need? And what are the differences? They all have to do with digital information, right?
While data is the common denominator between these three processes, they are actually different. Read on to learn the similarities and differences between these three concepts, and why that matters.
Data integration refers to the process of “the collection and integration of electronic transactions, messages, and data from internal and external systems and devices to a separate data structure for purposes of cleansing, organizing, and analyzing the joined data.” With data integration, you see a unified view of your data, because data integration software brings your information together.
One of the things that data integration has in common with data migration and ETL is that it is a process businesses use on their information. They are typically implemented when organizations want to get more out of their data; the organization can gain greater insights when information from various sources is combined.
During data migration, information is transferred. This transfer could take place in a few ways: between data storage systems, data formats, or computer systems.
One of the differences between data integration and data migration is that in the first process, you bring a variety of disparate information sources together, whereas, during the second process, the information already exists; it is simply being moved from one place to another.
“During data migration, information is transferred from one source to another.”
When do you engage in data migration? Data migration takes place when you are moving from one system to another (let’s say you are upgrading a mission-critical software system) so that the new system can contain the information from the previous system. Unlike with data integration, you will not gain any new insights from your migrated information.
ETL stands for Extract, Transfer, Load. This three-step process takes place when you want to move information from one system into a data warehouse environment.
Data migration and ETL are somewhat similar in that they involve moving information from one source to another. However, data migration does not involve changing the format, whereas ETL does (that is why there is the word “extract” in its name). As mentioned earlier, ETL and data integration are both used when organizations want to get more out of the information they have. But, data integration does not involve transforming information, either.
“Data migration and ETL both involve moving information from one source to another.”
Let’s take a moment to discuss the transformation aspect of ETL. To generate reports, information might need to be standardized, deduplicated (excluding and discarding redundant data), verified, and sorted. The information coming into the data warehouse is raw, and thus less useful, in its pre-transformation state.
Understanding the differences between data migration, data integration, and ETL is not just an academic exercise; it helps you make the right choice with your information. If you do not use the right process on your information, you will not see the success you want.