Data Integration

Before you start deep-diving into PocketQuery, we want to give you a short overview of the theory behind it. Theory?! Don’t worry! We won’t go into boring details, but will only roughly illustrate the main concepts of “Data Integration” for you. This will provide you with a solid intuition for what PocketQuery is capable of and what it can be used for.

Put simply, Data Integration really just means bringing or displaying data from different sources in one single system. There’s two ways that this is commonly done: Data Warehousing and Data Mediation. As PocketQuery performs the latter, we’ll only look at that.

In the world of Data Mediation, you have three important roles:

• External Datasources

• The Data Mediator

• The Target Application

The Data Mediator is responsible for pulling data out of the External Datasources, processing it, and displaying it in the Target Application. So, in our context, PocketQuery is the Data Mediator, pulling data from External Datasources and displaying them in our Target Application: Confluence. Thereby making Confluence a true single source of truth.

As you can see, PocketQuery is able to combine Datasources of many different types. Across Datasources, there can be big differences in type of connection, data layout, and therefore essentially the language PocketQuery needs to speak. For this to work, while still being easy to use, PocketQuery needs to provide a layer of abstraction over this process that allows you to interact with external datasources in a simple and uniform way. Want to learn how PocketQuery does this exactly?

Continue with “The PocketQuery Entities“