Data integration is the process by which data moves among distinct databases that are internal, external, or both. There are many technologies to use when it comes to this task, and you can understand how data traverses databases by learning more about data integration.
As per its pithiest definition, data integration focuses on putting data together in a way that discrete sources materialize into a more immediate view. One-sentence definitions don’t always tell the whole story, so let’s dive into other examples of pertinent information with regard to data integration.
What Is The Essence of Data Integration?
Most simply, data integration is what happens when data moves from certain databases to specific target systems. There are many types of databases that may be relevant when it comes to the effective integration of data. These databases include data warehouses, third-party tools, production databases, and others that generate and store data. Data integration ultimately consolidates data into centralized locations for the sake of organization and accuracy. The original purpose of data integration software was to address the rapidity with which companies were beginning to employ relational databases in opposition to other forms of databases. As more and more companies decided to employ relational databases, teams responsible for data management and collection needed to find some kind of way to avoid performing the inherently tedious task of manually inputting data. Obviously, they had to accomplish some sort of goal in the way of simplifying integration efforts.
The Importance of APIs
What Are The Different Types of Data Integration?
Let’s talk about some key items one might find across the landscape of data integration. Say you’ve come across an integration platform as a service (also known as iPaaS). Such a service shuttles data among cloud apps without offering much transformation across each datum. Another item is a customer data platform, which moves data among cloud apps by employing a central hub with which to enable the moderate transformation of the odd datum. Third, there is the Extract, Transform, and Load technology. This technology moves data from cloud apps to warehouses of data by employing a robust layer of transformation, and building that transformation layer into an ETL tool. ETL allows for data to be changed before data is loaded into a data warehouse. This is not to be confused with the Extract, Load, and Transform technology, which is different in the sense that it calls for the use of SQL, a robust and popular programming language for data management. Requiring SQL, the ELT technology allows for data to undergo transformation after being appended to a data warehouse instead of before. Finally, there exists Reverse ETL, and this is different from both previous methods, as it is the process by which data moves from data warehouses to cloud apps instead of the other way around. It’s hard to immediately internalize this many items at once, but hopefully, you have a general idea of what they are.
These definitions are the veritable tip of the iceberg. They are not all there is to the process of data integration. It is an enigma that requires a lot of skill to master, and you need to be willing to do a lot of self-teaching and studying. With any luck, this post may be able to assist you in terms of the process by which you might have a clearer understanding of what data integration really is. What’s also important is to recognize the pros and cons of these myriad technologies of varying usage. Clearly, there are multiple ways for you to address whatever specifications your business presents. What may greatly affect your decision as to which data integration process works best will be twofold: what your business needs, and what it can actually manage to do. Integration technologies are important for any business that wants to have better control over its management of data. Many businesses are still making the intrepid leap from analog to digital in many capacities, so do not worry if data integration remains to be something that is difficult. In time, it won’t be. Everyone who pursues data integration eventually figures it out. You need only to put in enough work, and you can have a great data integration experience as long as you understand how data integration works.