Time-series data is a sequence of data points collected over time intervals, allowing us to track changes over milliseconds, days, or years. It has become increasingly important due to the rapid collection and analysis of more data than ever before, with applications in self-driving cars, autonomous trading algorithms, smart homes, transportation networks, and COVID-19 statistics analysis. The demand for time-series data is growing rapidly, driven by the need for better decision-making, faster insights, and scalability. Time-series databases are designed to handle this high-volume data, providing built-in functions and operations that make it easier to analyze trends, optimize performance, and mine value from the data. By treating time as a first-class citizen, these databases offer massive scale, improved ingest rates, and faster queries at scale. They also provide specialized functions like time_bucket() that enable easy aggregation and analysis of data across almost any interval, making it easier to query meaningful insights. With the right tools, businesses can unlock valuable insights from their time-series data and make informed decisions faster.