When to use application integration versus data integration

[seonajmulislam00]

Application integration and data integration are two key processes for data scalability. When should you use each of them?

A process for every need. When it comes to data analytics, there are different ways of working in different contexts. Application integration and data integration are two ways of approaching this issue with different benefits for organizations.

Application integration or API is a set of rules, functions and procedures that allow you to access the backend of third-party applications to extract information and use it in your own application.

API is often used to achieve scalable data analysis, as it is a great intermediary between different applications that are geographically distant.

Data integration, on the other hand, is the process of bringing together data from multiple sources into an organized system to provide complete, accurate, and always up-to-date data for BI, analytics, specific business processes, applications, etc.

Modern enterprises use data integration when they need to canada phone number lead mobilize and manage large volumes of diverse, unstructured or semi-structured information. This means that the integration system used requires intelligent big data pipelines that can automatically move, consolidate and transform data from multiple sources in real time.

The benefits are many :

It allows you to bring together data silos into a unified and reliable source of 100% governed data.
Maximize opportunities to identify patterns and obtain actionable information that improves the performance of different business areas.
Promotes greater accuracy and confidence in information by providing a single, true source of accurate and 100% available data.
It promotes collaborative, data-driven decision-making because it encourages the participation of all users who are part of the company, turning them into active and essential factors in data analysis that they can easily govern.
It increases the efficiency of all processes and tasks developed in the organization, since time will not be wasted in collecting and preparing data manually.
When to use each approach?
Application integration and data integration are two approaches that businesses can implement to make use of their data. However, each approach meets different needs. We invite you to learn when you should leverage each one.

Application integration
When business processes span a multitude of applications , data must move from one application to another, almost in real time.

A clear example is when an organization onboards a new employee and their data starts in an HR system, but then needs to be integrated into other operational applications. Application integration software can orchestrate this process .

How does it work? The application integration-based approach sends instant data between multiple OLTP (online transaction processing) applications, from one point to another, allowing one application to serve as a source in a given process and a destination in another.

Data integration
Data integration , on the other hand, is responsible for compiling data from multiple sources into a single data repository .

Typically, data is replicated in a data warehouse for analysis and reporting, although this approach can also be used for migration and consolidation of operational databases .

The data flow in data integration occurs from sources to an analytics repository, data warehouse, or data lake. Unlike application integration, data integration does not necessarily require knowledge of business processes – it only needs data sources and a destination.

Now, it is not a question of one approach being better than another, but rather that each one, through its particularities, responds to a different purpose. In fact, application integration can be used to work with data at the application level and data integration can be implemented by working at the database level .

The Qlik Data Integration Solution
Qlik Data Integration is Qlik's platform that brings together data integration and application integration to deliver actionable, analysis-ready data from any environment and available in real time.