Using MongoDB with Hadoop & Spark involves integrating these tools to analyze large datasets. A Python script using pyspark leverages the benefits of Spark, which includes faster query results and more options for analysis compared to Hive. However, it may have a significant learning curve due to its complexity. Real-life applications include storing and querying real-time market data in MongoDB, where prices update throughout the day, allowing for fast access to historical data. The MongoDB Hadoop Connector makes it easy to integrate these tools, providing low latency, rich querying, and flexible indexing support, which enables technology teams to focus on data analysis rather than integration.