Real-time tick data applications, exemplifying real-time analytics, involve processing high-frequency event streams with minimal delay, crucial in financial markets where even slight lags can affect trades. ClickHouse is well-suited for handling such workloads, offering efficient data compression, time-based queries, and real-time data processing capabilities. This guide details building a real-time tick data application using Polygon.io for market data access and ClickHouse for data storage and querying, employing NodeJS for backend operations and React for live visualization. The text explains the differentiation between quotes and trades, the importance of WebSockets for data streaming, and efficient data modeling in ClickHouse. It further highlights the significance of optimizing ingestion methods, whether synchronous or asynchronous, and offers practical strategies for scaling, latency monitoring, and using materialized views for pre-aggregating data to optimize query performance. Essential SQL queries for real-time data visualization are provided, focusing on constructing live trading tables and candlestick charts, with an emphasis on maintaining system performance and reliability amid growing data volumes.