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Stream Processing is taking action on data when data is created. Previously, data practitioners utilized real-time processing to discuss data, which was processed as needed in a specific use case. However, with the adoption and advent of stream processing frameworks, it is being used specifically. 

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What is Stream Processing?

Stream Processing is a processing paradigm, which refers to processing data in motion or computing data directly when it’s received or produced. Most data are continuous streams such as sensor streams, financial trades, user activity, and others. These data are a series of events. Before stream processing, data was stored in a file system, database, or other kinds of mass storage. The applications query data or compute data if needed.


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A distributed stream processing system includes geographically distributed architectures to process huge data streams to enhance reliability and efficiency of data ingestion, data display, and data processing. It refers to the ability of an organization to process distributed data streams centrally from different geographically sources including cellular data towers and IoT connected devices.

Use Cases of Stream Processing

Some of the common use cases of Stream Processing are discussed in our Stream Processing Homework Writing Help

Real-Time Fraud Detection: Credit card providers have been able to minimize fraud and detect anomalies by stream processing. Delays in credit card processing are detrimental to both end-customers and stores that are attempting to process credit cards. Earlier, credit card providers performed fraud detection in a batch way. With the help of stream processing, the moment you swipe the card, they can run algorithms for recognizing fraudulent charges and also triggers alerts that need additional inspection minus making their customers wait.

Real-Time Marketing, Personalization, And Advertising: With stream processing, companies deliver contextual and personalized experiences for customers. It might include discounts for something you have added to carts on the website but did not purchase immediately, the recommendation for connecting with a friend on social media site, or advertising for products you have seen.

Iot Or Internet Of Things Edge Analytics: Companies in oil and gas, manufacturing, transportation, architecting smart buildings and smart cities use stream processing to keep up with data from a lot of things. One of the examples of IoT is finding out anomalies in manufacturing, which indicate problems, which need to be fixed for increasing yields and improving operations. In stream processing, manufacturers can recognize that production is turning out a lot of anomalies when it happens. They can prevent huge waste and recognize massive savings by interrupting a line for instant repairs.


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When is Stream Processing Used?

Stream processing should be used when you look for analytics in real-time. Stream processing is effective for situations that need in-built features and minimal latency to handle imperfect data, the ability for generating repeatable and predictable results, SQL queries to create extensive operators, data safety, streaming and stored data integration abilities, the ability for scaling applications among several nodes and processors.

The applications where stream processing is highly effective are stock market surveillance, algorithm trading, wildlife tracking, network monitoring, sports analytics, smart patient care, traffic monitoring, and others. These are highlighted in our Stream Processing Assignment Help.

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