Real-world Java and big data in action

Delving beyond the theoretical, we’ll delve into three practical use cases that showcase the power of this combination.

Use case 1 – log analysis with Spark

Let’s consider a scenario where an e-commerce company wants to analyze its web server logs to extract valuable insights. The logs contain information about user requests, including timestamps, requested URLs, and response status codes. The goal is to process the logs, extract relevant information, and derive meaningful metrics. We will explore log analysis using Spark’s DataFrame API, demonstrating efficient data filtering, aggregation, and joining techniques. By leveraging DataFrames, we can easily parse, transform, and summarize log data from CSV files:

public class LogAnalysis {
    public static void main(String[] args) {
        SparkSession spark = SparkSession.builder()
   ...