Moving data between databases sounds simple in theory. However, there are so many different types of data, databases, and methods of data integration to consider. Since this is the case, having a decent grasp of data and databases is important in order to understand the proper data integration to implement. The world of technology and business is ever changing and colliding. This collision has allowed for ample innovative opportunities. One of the spaces of innovation that has drastically improved and evolved is data integration. Without data integration, data cannot move between databases.

What is Data Integration?

While data integration may sound like an intimidating term, it actually comes with quite a simple definition. Data integration means the process of data moving to a target system from two possible places:

• Internal databases
• External databases

In this context, databases can refer to:

• Production DBs
• Data warehouses
• Third-party systems

Whatever the database may be, the main goal stays the same. Integration focuses on taking differing data and sending it to a central location. These efforts were born out of the deep desire for manual data entry to be a thing of the past. Regardless of the integration efforts, it is important to understand that every type of integration uses some sort of API. These are all the essential features that play a role in integrating data between databases.

Data Integration Options

When it comes to integrating data, there are so many different avenues to consider. Let’s break down all of your options so that you have a decent understanding of what’s available to you. From there, you can make an informed decision about what works best for your business efforts.

• Integration Platform as a service, or iPaaS: all the data possible or necessary simply moves between cloud apps. Very little transformation occurs in the iPaaS.
• Customer Data Platform, or CDP: all of the data is capable of moving from cloud apps by use of a central hub. This central hub enables some transformation capabilities.
• Extract, Transform, and Load, or ETL: data can move from a cloud app and a data warehouse. This transaction is possible due to a very intense transformation layer that is built within the ETL tool. This integration model allows data to be transformed before it ever enters the warehouse.
• Extract, Load, and Transform, or ELT: this integration model primarily functions by moving data from cloud apps to a data warehouse. The difference with this model is that transformation and data modeling happen by using a method called SQL. SQL allows the data to be transformed after it enters into the warehouse.
• Reverse ETL: this is an integration method that does the opposite of all the other methods. The aforementioned methods function by inputting data from cloud apps into a warehouse. Reverse ETL focuses on taking the data from the warehouse and putting it into the cloud apps. Generally speaking, the transformation process occurs within the data warehouses before the data is transferred to a cloud app.

Now that you know what all of the integration methods do, let’s explore how this impacts your business.

Data Integration for your Business

Every integration option available to you comes with their own set of features, strengths, and weaknesses. It’s important to understand each of these, as the features will affect the nature of your business. For example, iPaaS is a simpler integration model. This method is based on the event, or trigger, eliciting a response within the system. Once a set of actions takes place, the iPaaS solution takes over. However, iPaaS also comes with some downfalls, as most things do. While iPaaS excels when events or triggers take place, it falls short if there is any data change that has nothing to do with an event occurring. Because iPaaS is so flexible and simple, a lot of the complex programming is up to the user. IPaaS doesn’t account for any changes or specifications that are unique to each individual user or business.

Another popular model that is used is the Reverse ETL model. A feature that exists within this model is the universality. There are new tools and databases invented, what feels like, every day. Reverse ETL’s ability to transform data from numerous sources is what sets it apart from the rest. It is especially useful when inputting or transforming customer data. This is because customer data can be displayed across both internal or external databases. This makes the Reverse ETL more versatile. Every time data changes, the API is prepared and activated to account for the change. This is a feature that is particularly unique for data integration.

The more you Know

Understanding how data moves between databases can seem like speaking another language. However, it is built on a simple concept. What isn’t so simple is trying to decide what is best for you and your business. Take a deep look at every integration method and decide what best suits your needs.