This primer on time-series databases explains the characteristics of time-series data, why specialized time-series databases are better at storing time series, and how they can help with scale and usability. Time-series data is typically stored in a database that leverages its foundational characteristics to store it more simply and efficiently than general-purpose databases. Specialized time-series databases offer massive scale, from performance improvements, including higher ingest rates and faster queries, to better data compression. They also provide built-in functions and operations common to time-series data analysis, such as data retention policies and continuous aggregate queries. These features enable developers to discover opportunities they didn't know existed and make data analysis tasks easier. With the increasing need for databases that can store high-granularity and high-volume datasets from IoT devices, companies are investing more in data and analytics, driving the demand for time-series databases. By storing time-series data in a specialized database, developers can simplify their data infrastructure, improve data ingestion performance, and simplify querying, while automating data management tasks such as compressing old data to save on storage costs. TimescaleDB is a PostgreSQL extension that extends its capabilities to handle time-series and analytical workloads with features like auto-partitioning, native SQL support, scalability, indexing, hyperfunctions, and an ecosystem compatible with various tools and technologies.