To put it another way, our solutions were developed to automate the transfer of data from one system to another. Although the term “dataflow” is used in a number of different contexts, for the purposes of this discussion, we will use it to refer to the controlled and automated flow of information between systems. This problem space has been around ever since businesses started using more than one system, with some of those systems producing data and some of those systems using data for their operations. Extensive discussion and articulation have taken place with regard to the problems and solution patterns that have emerged. The Enterprise Integration Patterns (also abbreviated as eip) contains a form that is both comprehensive and easy to consume.
The following are some of the more fundamental difficulties associated with dataflow:
The system is broken.
Problems can arise with networks, discs, and software, and people can also make mistakes.
The access to data far exceeds the capacity to consume it.
It only takes one vulnerable link in the chain for there to be a problem; sometimes one data source can outpace another part of the chain that processes or delivers the data.
Simply put, boundary conditions are nothing more than suggestions.
Inevitably, you will obtain data that is either too large, too small, too fast, too slow, corrupt, incorrect, or presented in the incorrect format.
Why is there so much noise? The priorities of an organisation shift from one day to the next, and this shift can happen very quickly. The facilitation of new flows and the modification of existing ones must take place quickly.
The rates at which systems evolve can vary.
Any given system is free to make whatever protocol and format adjustments they see fit at any time, regardless of the other systems in their environment. Dataflow was developed as a means of connecting what is essentially a massively distributed network of individual components that were only haphazardly or not at all planned to operate in tandem with one another.
Regulatory conformity and safety
Laws, regulations, and policies are all subject to revision. Alterations are made to agreements made between businesses. Interactions between different systems and between different systems and users need to be secure, trusted, and accountable.
The manufacturing process undergoes continuous improvement.
In many cases, it is simply not possible to come anywhere near replicating production environments in the laboratory.
Through the years, dataflow has become one of those undesirable aspects of an architecture that are necessary. However, there are a number of active and rapidly developing movements that are making dataflow a great deal more interesting and a great deal more vital to the success of any given business. These include things like Service Oriented Architecture (also known as SOA), the rise of the API (also known as API) and API2, the Internet of Things (also known as IoT), and Big Data (also known as bigdata). In addition, the level of stringency that is required to ensure compliance, privacy, and security is continually increasing. Even with all of these new ideas being developed, the patterns and requirements of dataflow have remained largely unchanged for the most part. The primary distinctions then are the magnitude of the complexity, the rate of change that must occur in order to adapt, and the fact that, at scale, the exceptional case becomes the norm. NiFi was developed specifically to assist in overcoming the difficulties associated with modern dataflow.
- With more than 450 agnostic connectors at your disposal, you can transport data in any structure from any source to any destination in a seamless manner across hybrid environments.
- You can get the most out of your resources by streamlining your architecture, avoiding the trap of data lock-in, and cutting down on the proliferation of tools and redundant data movement.
- You can reach the next level of agility by enabling no-code developer self-service across the entire lifecycle of the data pipeline.
Photo by Aleksejs Bergmanis: https://www.pexels.com/photo/aerial-photo-of-buildings-and-roads-681335
