Online Analytical Processing (OLAP) software is designed to enable detailed analysis of vast amounts of business data from diverse sources, helping organizations gain critical insights into operational performance and profitability. OLAP systems must overcome challenges such as dispersed data and the need for compatibility with various technologies, often employing ETL or streaming methods for data intake. These systems are adept at handling large data volumes and executing resource-intensive operations, although the rise of real-time analytics is pushing OLAP solutions to adapt for faster, more concurrent processing. OLAP databases fall into three categories: Multidimensional OLAP (MOLAP), which uses data cubes for intuitive analysis; Relational OLAP (ROLAP), which structures data like traditional SQL databases; and Hybrid OLAP (HOLAP), which combines attributes of both. Each type facilitates core OLAP operations like rollup, drill down, slice, dice, and pivot to provide different data perspectives. OLAP remains popular across industries due to its ability to democratize data analysis, offering powerful insights without requiring deep technical expertise, and enabling organizations to streamline processes and improve decision-making. Apache Druid exemplifies a modern OLAP solution by combining fast response times with the ability to handle complex queries at scale, supporting both transactional and analytical needs through features like schema autodetection and integration with streaming platforms like Amazon Kinesis and Apache Kafka.