Time-series data is ubiquitous, driving decision-making across various industries. Developers collect vast amounts of time-series data and struggle with basic analysis due to the lack of specialized tools and skills. Excel, R, and Python are commonly used for data analysis but are not optimized for speed or efficiency, particularly when dealing with large datasets. A database built specifically for time-series data can alleviate these issues. TimescaleDB, a relational database for time-series data, offers continuous aggregates, compression, and hyperfunctions, allowing developers to perform data munging tasks directly within the database, reducing the need for additional tools like Excel or Python. This enables efficient management of scripts, easy adoption of new technologies, and improved collaboration among team members. By leveraging TimescaleDB functionality, developers can streamline their data analysis workflow and tackle complex problems more effectively.