How Does Inherent Parallelism in Processing Improve Throughput?

From the very beginning of developing the product, our idea has always been to develop something which is flexible, extensible, and user-friendly. But if you think of integration between disparate and diverse applications, joining them together is not an easy task. You need a lot of advanced tools and features that can enable you to develop faster and better integrations between applications. One such capability which you can use during execution is Parallelism.

What is Parallelism?

Parallelism is a concept where a process is broken into multiple smaller chunks such that each of the processes can work in parallel to perform a bigger task. For example, let us suppose you want to sync 1000 data at one point in time. Now, you cannot download all the data at a time, and it requires you to create batches, such that it downloads each chunk one after another. Now if you make a batch of 100 items, it requires the application to download 10 times. Here we need a loop. But if it is done sequentially, then that means it will first download the first 100 items, process them, and then start processing the second batch, process the next 100 items, and so on. Without Parallelism, if you implement such a concept, your data processing throughput will be much slower.

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