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Data Streaming Use Cases And Applications
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Data Streaming Use Cases and Applications

What Is Real-time Data Streaming

Real-time data streaming is a process that filters and infers data to accomplish tasks at the time it is published or generated. Businesses use real-time data streams to incorporate modern technologies like IoT, advanced machine learning, and artificial intelligence. These are critical in improving human-machine interaction, solving business problems, and achieving a data-driven personalized customer experience. In this article we will understand what is Data streaming & will see some use cases.

Data Streaming: Use Cases and Applications

Real-time data streams have been created to support a variety of use cases through faster and efficient data movement. It has come a long way from being used as a messaging system alone. Infact, this use-case amounts to less than 5% of the event streaming platform Kafka’s deployments. Data streaming using Kafka facilitates companies to use data streams in ways that reform their businesses to match the technology-driven consumer demands.

However, to accomplish a variety of use-cases, data streams must be able to:

  • deliver real-time analytics,
  • carry relevant information,
  • perform real-time anomaly detection, and most importantly,
  • must be embedded with data security.

Data streaming use cases for different applications

Some of the prominent data streaming use cases in various industries for different applications are:

1.   Network Monitoring

With digitization, the network and infrastructure have become more complex than ever. It poses a challenge for administrators to efficiently manage and maintain the system. Data streaming helps them in monitoring the network as it can capture, analyze and process millions of events from device logs in the network. It provides a tool in creating in-depth log analysis reports with data visualization of streams and helps in extracting meaningful data from the log files.

2.   Customer Experience

Companies across industries are leveraging real-time data streaming to gain comprehensive insights about customer behavior and choices. Data streaming using Kafka has helped many organizations promote a better understanding of the same through an extensible and scalable data stream infrastructure. The continuous data sources are captured, and the real-time metrics are used to provide a personalized customer experience.

3.   Cyber Security

One of the most imperative data streaming use cases is in preventing and predicting cyber attacks. Data streaming using Kafka is a boon to business as it helps to identify the security issues in real-times.
Real-time data streaming helps cybersecurity decision-makers as well. It filters data from storage and integrates them into a robust data analytics platform. Often Kafka security is combined with Security Information and Event Management (SIEM) or machine learning to analyze traffic and identify common cyber attacks like DDoS.

4.   Fraud Detection

Real-time data stream processing helps developers write applications that can detect fraud, especially for financial service companies. It aids the companies in identifying anomalies and detecting fraudulent transactions. Real-time data analytics helps cyber experts to inspect, correlate and analyze data at all times and take immediate action in case there is a possibility of cyber fraud.

When combined with machine learning, large data sets can be analyzed to figure out a pattern that eventually helps in accurately identifying and preventing such untoward happenings.

Data Streaming: Real-life examples

Cerner, an IT services company, deployed data streaming using Kafka and IBM Streams. They monitored 1.2 billion real-time monitoring system (RTMS) daily timers and used the data to gain faster and detailed insights into the performance abnormalities.

With this, they were able to detect signs of performance degradation and help their clients with accurate and reliable online health care systems around the clock.

Verizon applied the data stream use case for an enhanced customer experience by acting upon customer queries as fast as possible. For this, it converted the customer’s conversion into text, analyzed the information to identify the key areas of concern.

As this is all done within seconds, the call center gets the required information to resolve a customer’s issue within minutes. Thereby increasing the overall customer satisfaction.

To sum it up

Real-time data streams were used as a real-time messaging medium for a long time. Now it is finding applications across industries for mission-critical, data-driven use cases to improve their businesses holistically.

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