The text explores the decision-making process organizations face when choosing between building a custom security analytics solution or purchasing an existing one, emphasizing the critical role of customization, scalability, and cost in this decision. It highlights the evolving nature of cybersecurity threats, such as social engineering and DDoS attacks, and the importance of rapid detection and response. Apache Druid is presented as an ideal database solution for in-house security monitoring due to its speed, scalability, and compatibility with streaming technologies like Amazon Kinesis and Apache Kafka. The text also provides a case study of the Development Bank of Singapore (DBS), illustrating how Druid helped the bank achieve real-time compliance, enhance data retention, and improve fraud detection through advanced analytics and machine learning capabilities, ultimately leading to faster and more efficient operations.