Scenario: Your team is tasked with designing a big data storage solution for a financial company that needs to process and analyze massive volumes of transaction data in real-time. Which technology stack would you propose for this use case and what are the key considerations?

For this use case, I would propose a technology stack comprising Apache Kafka for real-time data ingestion, Apache Hadoop for distributed storage and batch processing, and Apache Spark for real-time analytics. Key considerations include the ability to handle high volumes of transaction data efficiently, support for real-time processing, fault tolerance, and scalability to accommodate future growth. Apache Kafka provides scalable and durable messaging, Hadoop offers distributed storage and batch processing capabilities, while Spark enables real-time analytics with its in-memory processing engine. This stack ensures the processing and analysis of massive transaction data in real-time, meeting the requirements of the financial company.