# The AI-Powered Evolution of Big Data
## 4 Materials and Methods
### Traditional 4 Vs of Big Data
#### 4.2.2 Velocity  
The velocity in the spectrum of Vs signifies the rate at which data are generated and handled. Due to the widespread use of IoT devices, social media platforms, and sensors, data are now being generated at an unprecedented rate, which requires fast processing capabilities. Rapid data analysis is essential in high-data-velocity contexts, as evidenced by the influx of tweets during significant events such as elections or athletic contests. The flood of data presents a challenge to conventional data processing systems, as it becomes increasingly difficult to collect, store, and evaluate data in real time. The escalating increase in data volume amplifies this obstacle. The velocity of data can be mathematically determined as follows:  
\[\textit{Velocity of Data = Rate of Data Generation/Processing Time} \tag{1}\]  
A high data velocity necessitates tools that can keep pace with the incoming data stream, enabling real-time analysis and decision-making. This is where AI and LLMs, known as \"Big Calculators\", come into play. Their ability to rapidly process and extract insights from vast amounts of data in near real time addresses the velocity challenge. Apache Kafka and Spark Streaming hold significant importance in terms of managing high-velocity data streams. Kafka serves as a mediator, managing large amounts of data through its distributed structure and via its ability to process data quickly. On the contrary, Spark Streaming operates by dividing data into smaller chunks, enabling prompt analysis and transformation. Despite this, the convergence of AI and LLMs holds the key to the future of Big Data analytics. ChatGPT language models can undergo training using extensive datasets in order to comprehend and analyze real-time data streams effectively. This feature provides new opportunities for instantaneous sentiment analysis, trend recognition, and anomaly detection, even during intense data output. Figure 7 demonstrates the integration of AI into [PERSON]'s cluster. Velocity encompasses more than just the rate at which data are generated and processed. It also encompasses the requirement for AI models that are agile, adaptable, and capable of responding to changes in data distributions and patterns as they arise. It is crucial to possess the capability to manage data streams that are rapidly moving and extract immediate insights in the age of Big Data. Although traditional technologies such as Kafka and Spark Streaming offer valuable solutions, the future of Big Data analytics hinges on harnessing the potential of AI and LLMs. Let us examine the use of Twitter during significant events, such as elections and sporting events. Countless tweets  
Figure 7: AI integration within a Kafka cluster. sphere every minute, creating an immense data surge. We require expeditious instruments to comprehend emotions or discern patterns promptly. In this context, Apache Kafka and Spark Streaming are introduced. Apache Kafka acts as a protective barrier, effectively managing the chaotic flow of data using innovative techniques. The formula for the throughput is as follows:  
\[\textit{Kafka Throughput}=\textit{Producers}\times\textit{Messages per producer}\times\textit{ broker} \tag{2}\]  
In Equation (2), producers are data sources from which information flows originate. The number of messages per producer reflects the number of messages each source produces, representing the data generated. The brokers manage this data flow as intermediaries in order to collect and distribute messages. Kafka can handle higher throughputs with more producers or brokers, as shown in Figure 7. A cluster Apache Kafka architecture, like having additional lanes on a highway, allows for the release of additional data without causing a blockade. Spark Streaming has its own approach, breaking tasks into smaller bits using the magic formula: Spark Streaming throughput equals the batch duration times the number of cores.