What is event Streaming in Apache kafka?

I started studying about Apache Kafka, which is widely used in industries such as IoT, finance, social media, eCommerce, and advertising. These are my notes, which I gleaned from many web sources in order to have a better knowledge.

Generally used to record events that occur on both the user and server sides, so that we can use these events or messages in our data processing or analytics.

What is even Streaming: The technique of gathering data in real-time from event sources such as databases, sensors, mobile devices, cloud services, and software applications in the form of streams of events, as defined by Apache Kafka, is known as event streaming or messaging.

Simply defined, data is what a 'event' is. But, more specifically, an event is a change in the state of data, such as a sensor reporting a change in temperature, a field in a database changing, a bank transfer being finished, a checkout button in an e-commerce app being pressed, and so on. These might be system-level or business-level events. All of the occurrences are legitimate.

To put it another way, event streaming enables organisations to evaluate data related to an event in real time and respond to that occurrence.

For example: When a business transaction occurs, such as when a client places an order or a deposit is made to a bank account, it triggers the following step.

1. Using unused data.
Everywhere you look in a business, there is a huge quantity of data.
Manufacturing businesses, for example, contain information on machine failures, time to completion, capacity peaks and flows, consumption information, and so on. Customers' wait times, plane delays, maintenance records, and ticket purchase trends, among other things, are all available to airlines. So much of this information is currently gathering dust. That data may be put to good use by businesses.

2. Taking use of data insights that are available in real time.
Real-time information and the capacity to react to these insights are two fundamental principles of event streaming. Let's say a consumer is looking for a new TV online, but it's out of stock. It doesn't help the store if they learn about the data a week later. The consumer has already moved on to another location.

Companies should be able to benefit from real-time data. For example, if a consumer frequents a certain business, a store can send a tailored ad or voucher depending on the client's location utilising location data from mobile phone traffic or public wifi.

3. Creating more engaging and better customer experiences.
When a company combines the flood of data with real-time data insights, it has the potential to provide consumers with better and more engaging experiences.

With so many options available to customers these days, gaining hearts and eventually business requires not only the finest product, but also the best and most engaging customer experience. Companies may establish new methods of connecting with their consumers by responding to circumstances as they arise, improving customer sentiment.

Consider the case of a commercial airline. When flights are cancelled or delayed, customer support representatives and desk agents are inundated with angry passengers. Employees with event streaming capabilities can notice the incident, in this example a cancelled flight, and respond in real time by rebooking customers on similar itineraries, improving the customer experience.

Manipulation, processing, and responding to these event streams in real-time as well as retroactively; and routing the event streams to various target technologies as needed.

Event Stream Processing (ESP):

 Event Streaming:

• Batch processing entails performing operations on a huge amount of static data (also known as "data at rest")..
• The goal of event stream processing is to take action on a continuous stream of data (also known as "data in motion")..
• In circumstances when action must be done as soon as feasible, event stream processing is required..
• This is why “real-time processing” is commonly used to characterise event stream processing settings..