A primer on time-series data, what it is, where to store it, and how to analyze it to gain powerful insights. Time-series data is a sequence of data points collected over time intervals, allowing us to track changes over time. It can track changes over milliseconds, days, or even years. Traditional data often missed the nuances of these changes. However, with modern technology, we can capture and analyze more data faster than ever before. This has led to applications such as self-driving cars, autonomous trading algorithms, smart homes, transportation networks that fulfill lightning-fast same-day deliveries, and tracking daily COVID-19 statistics and air quality in communities. Having access to detailed time-series data has become one of the most valuable commodities in our information-hungry world. Businesses, governments, schools, and communities are finding invaluable ways to mine value from analyzing time-series data. Software developers are increasingly adopting time-series databases due to their ability to handle high-resolution data and provide efficient querying capabilities. Time-series databases offer scale, usability, and specialized functions that make it easier to analyze data in meaningful ways where time is a primary component. They can help with performance improvements, including higher ingest rates and faster queries at scale. With the rise of self-service business intelligence tools, there's a growing need for time-series databases to provide timely insights without relying on third-party tools. Not all data is time-series data; however, many applications have inherent time-series nature that can be harnessed with modern technology. Logging additional details about user logins can quickly transform our ability to understand how our web application is used and make decisions faster. To get started with time-series databases, one can spin up a fully-managed TimescaleDB instance for free and follow the getting-started guide to configure the database and execute the first query.