Data integration is the process of acquiring data from any disparate data sources such as databases, services, processes, applications, UI, and so on and processing, and creating and publishing an outcome.

data integration

Data integration makes the data more available to be consumed by people, processes, and systems without relying on IT. Therefore, it reduces costs, frees up resources, empowers leaders in business, operation, and technology to innovate without changing data, and application structure and architecture.

From companies’ standpoint, it enables them to create decisions faster than their competition, therefore:

  • It increases operational efficiency by reducing the manual and codifying data transformation
  • Facilitates application of rules and data quality

Apart from data-driven decision-making in strategic and tactical business decisions and building machine learning models from the data, there are other use cases for data integration such as data virtualization, Extract Transform and Load (ETL), Data Quality, and so on.

How does Data Integration Work?

There are multiple stages to data integration. As a whole, you will create a set of orchestration models that satisfy all the stages.


Acquiring data is the first stage of the overall data integration process and is a big problem to solve. Because traditionally, this is done by IT, who has the expertise to create queries, pull data, load files, access services, etc. But to empower the leaders to do the same, there are multiple components and user interfaces that should work seamlessly to make this happen.

Data Query

End-users require to connect to data sources, create queries and fetch the data from services, applications, databases, and files. Therefore, these components are needed to enable data acquisition:

  • Visual Query Builder: Create SQL queries visually
  • REST Services Connectors: Connect to REST API services
  • Database Connectors: Connect to any SQL and NO-SQL databases (On-Prem and Cloud)
  • File Connectors: Connect to different types of files such as CSV, EXCEL, JSON, XML…
  • Application Connectors: Connect to online applications such as GMAIL, Dynamic365, Salesforce, LinkedIn, Twitter and etc.
  • User Interface, i.e., UI Connectors (Web & Windows): Collect data from the application’s UI when they don’t provide standard data adapters using Robotics


The loaded data is consumed at the process stage, and the data integration use case is satisfied. Whether it is to use the data to make a decision or for use cases such as data virtualization, ETL, Data Quality, and so on.

As part of this stage, a set of outcomes can be created. This depends on the purpose of the data integration, which may vary case by case.


At this stage, the result and outcome are to be published to processes, people, applications, and services.

  • Do you need to notify a person of a report or abnormality detected?
  • Do you need to push the result to a dashboard?
  • Do you need to update and record the outcomes to applications or databases?
  • Do you need to push the results to other service endpoints and online applications?
  • Do you need to update and interact with desktop or web application UI?

The point of this stage is to ensure you close the gap to make the outcome operational.

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Data Integration made Simple!

With a no-code approach, data integration is done with the minimum IT involvement, and we put it in the hands of operation and business leaders. Hence the traditional data integration systems such as data warehouses or data marts are still valid for many organizations and scenarios; this approach involves using already available data, putting them in use, and testing without any change in the data and physical environment.

Published May 6th, 2022 at 01:54 pm