MongoDB, a favored NoSQL database for online transactional processing, is often transitioned to Apache Druid for enhanced analytical capabilities as data scales. This blog elucidates the process of migrating data from MongoDB to Druid, highlighting steps such as connecting to MongoDB, extracting data, and ingesting it into Druid. Druid, designed for high-performance analytical queries, uses a columnar format and features an indexing engine and segmented data storage to significantly improve query performance. The guide provides a Python script for data extraction from MongoDB, storing it in CSV format, and creating an ingestion specification for Druid. It also discusses using Change Data Capture (CDC) methods for ongoing data updates and explores tools like MongoDB Change Streams and ETL solutions for managing these updates efficiently. Druid's real-time data stream support and focus on swift analytical queries make it a powerful choice for real-time analytics, enabling faster data-driven decision-making.