The goal of Inflowenger is to provide a solution for integration, compliance, automation, and adapter in IT systems. It is utilized in environments where customers or users need to define the system logic. The Ideal of Inflowenger is to serve as a process flow ,data plane and supervisor for FAAS (Function as a Service) Cloud Computing.
In describing how Inflowenger works, this scenario can help me articulate its functionality,
Data enters a tunnel in as a document, which comprises windows functioning as nodes that we defined this as a policy. The data is manipulated by these windows if defined conditions are met, thereby effecting changes or generating fields on the document if necessary Although rules or nodes are capable of providing data, this is done by the restNode type, which refers to the endpoint. In this regard , Inflowenger can have extensions with transfer data protocols to perform certain functionalities within the process flow
Additionally, it would be beneficial to incorporate a form builder within the process flow, enabling the conversion of documents into a user interface based on entered data.
This feature is already provided to Front in the JS Library. Its inclusion will prove highly advantageous, particularly in projects like ERP and ERM, where both logic definition and front-end production are accessible to users.
One of the definitions of Inflowenger is that it serves as a document-centric rule engine with policy and rule capabilities. Considering the features of role and policy definitions, Inflowenger falls into the category of rule engines.
In the process of integrating external data with internal organizational models, Inflowenger can be used as a gateway to dynamically transform this data into the required format on the fly. It facilitates the conversion of data into the desired format as needed.
If we consider Inflow as an ETL tool, we gather data using extensions or RestNode. If the data needs structural changes, they will be applied based on the content or defined policies. Finally, in the last stage, we identify their destination and send them to the storage service. All ETL stages will be performed continuously or in loops, receiving data page by page, by defining nodes as Workflow Graph
In all systems, there exists a primary model, and with the help of extrinsic models, it is possible to provide supplementary data for subsequent stages. These extrinsic models can assist in supplying additional data to enhance the functionality of the system in subsequent stages
In environments where multiple stateless services exist, decision-making for the process flow can be delegated to Inflow. This allows for the utilization of a set of functions based on rules and the content of a document.
Inflow can indeed play the role of an interpreter and decision-maker to respond to events. These events can be generated from various sources, and their webhooks should be addressed to "inflowenger" gateway.
Inflowenger can be used in workflow systems based on artificial intelligence to transform user space data into the required values for neural networks. It can facilitate this transition and aid in converting data into the necessary format for neural networks.
In architectures like the Hexagonal Architecture, where adapters connect the core system with other domains, Inflowenger can be an extremely useful tool in such situations. It can aid in managing the communication and interactions between the core system and different domains, making it a valuable tool in such scenarios