Streaming Data Analytics Use Cases, Examples, and Architecture

What exactly is streaming data analytics? The continuous processing and analysis of massive data in motion is streaming analytics. Equipment sensors, clickstreams, social media feeds, app activity, stock market quotations, and other sources of streaming data are examples. Businesses use streaming analytics to identify and evaluate trends, produce visualizations, disseminate insights and warnings, and activate actions in real-time or near-real-time.


Streaming Analytics Use Cases and Examples


Streaming analytics is appropriate for processing data from sources that provide modest amounts of data regularly. Here are a couple of streaming analytics examples:



• Credit card fraud detection: In 2019, six card brands processed 440.99 billion purchasing transactions for goods and services. Card organizations, such as Visa or MasterCard, must evaluate billions of transactions and trigger alerts based on certain criteria in order to identify and prevent fraud. When correctly configured, a streaming analytics system can aid in the automation of fraud detection. Essentially, it achieves this by first determining if any of the payment authorization request's attributes match any of the business's criteria for what makes up suspicious conduct. If the request is deemed suspicious, the system may send an automatic text message to the cardholder requesting confirmation of the transaction.
• Efficient delivery truck routing: For logistics firms, efficiently routing vehicles is the entire business. However, continually changing variables, such as traffic and weather forecasts, determine the most effective route from point A to point B. In some circumstances, trucks are also used to carry temperature-sensitive products, such as medications. Temperature sensors, traffic conditions, and weather forecasts are all examples of streaming data that logistics firms may use to make better business decisions. However, if you want to analyze data rapidly enough for it to be effective, you'll require streaming analytics. After all, if the signal for an overheated vehicle arrives too late for the driver to act, the cargo may become useless.

Streaming Data Analytics Architecture


Many frameworks, programming languages, and analytics tools may create streaming data analytics architectures. However, a streaming analytics infrastructure must essentially be capable of:



1. Data from a streaming source should be captured. The "stream processor" or "message broker" is in charge of this.


2. To give the context, combine and analyze the acquired data. This is the stage at which data is pooled and converted, often using a streaming analytics platform.
3. Respond consistently and promptly based on the data processed. The use case determines this last component of your streaming analytics architecture. You might utilize an AWS data warehouse to transmit processed data straight to an application or dashboards and query it using SQL.

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