SQL is an ideal language for working with time-series data analysis due to its ease of adoption, expressiveness, and ability to join relational data with metadata. To get started, developers can use SQL schemas to organize their database objects into logical groups, making it easier to collect cleaner data and provide flexibility in data management. Additionally, tools like PostGIS, DBeaver, and PGAdmin can help developers work with time-series data by providing spatial functions, graphical query planning, and procedural language debugging capabilities. By applying SQL aggregate functions such as COUNT, SUM, AVG, MIN, and MAX, developers can quickly summarize their data to build reports that answer critical business questions about their data. With the right tools and resources, developers can unlock the full potential of time-series data analysis using SQL.