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This article, which was previously published on another platform is the second, in a series that aims to provide an overview of Enterprise Integration. Here we will explore the options for integration tooling and the potential challenges that enterprises may encounter such as creating tooling silos while trying to prevent data silos.
Enterprises make use of a range of applications and systems to store their data. Some examples of these applications and their integration methodologies include:
Cloud Applications: These applications usually offer REST or similar APIs for integration. In some cases specialized connectors can be developed.
Custom In House Applications: These are applications hosted within an organizations infrastructure. Integration methods may involve REST APIs or direct database access.
Databases: Integrating with databases often involves using SQL queries or specialized tools to enhance efficiency.
Data Warehouses: Similar to databases data warehouses also rely on SQL queries or specific tools for performance.
Data Lakes: These typically provide filesystem level APIs for inputting data.
There are options when it comes to integration tooling for working with these applications:
In House Integration Tools: These tools support ELT use cases, for data integration but can sometimes present maintenance challenges.
Different Types of Integration Solutions:
Custom Integration Solutions: These are applications developed in house, for integration purposes. They can be challenging to update or expand.
Message Queue Tools: These tools provide a message queue interface that facilitates application integration with a focus on developers.
Cloud Application Integration Tools: Primarily designed to integrate cloud based applications.
Infrastructure Service Tools: These tools concentrate on integration within infrastructure ecosystems.
API Management Tools: They assist in the development of APIs to expose application data.
Data Warehouse Tools: Mainly used for supporting the input of data into warehouses.
Big Data Tools: These tools are specifically designed for supporting the input of data into data lakes.
With the range of integration tools enterprises may unintentionally create tooling silos resulting in the need for multiple tools for specific integration scenarios and complicating the overall integration landscape. This fragmented approach can lead to organizational challenges emphasizing the importance of tool selection by enterprises.
An ideal modern enterprise should have an integration platform that's capable of handling various integration requirements effectively. This platform should offer support, for:
Data Integration: It should support file formats and authentication methods.
Application Integration: Native connectivity options should be provided for enterprise applications.
User Friendliness: The platform should simplify the integration process for non developers.
Hybrid Deployment: Achieving an integration of both cloud based endpoints.
Support for Big Data: Ensuring scalability to handle volumes of data.
API Management: Simplifying the process of creating and exposing APIs without relying on tools.
Data Science Capabilities: Empowering users to build, train and deploy machine learning models effectively.
If you can think it, we can do it.
