Time-series analysis is a powerful technique used by software engineers to extract valuable insights from temporal data. It involves analyzing and predicting patterns based on time-based data, which can be achieved using Python as the primary programming language for data analysis. Python offers an ideal choice due to its simplicity, versatility, and vast community support. By leveraging libraries like pandas, NumPy, statsmodels, and scikit-learn, developers can perform various operations such as data aggregation, filtering, and computing summary statistics. Additionally, Python provides extensive library support, allowing for code reusability and facilitating the development of time-series forecasting models. Common tasks in time-series processing include plotting data using Pyplot, performing time-series analysis for predictive questions, extracting useful features for machine learning/deep learning algorithms, data cleaning, and handling large datasets. To work with time series in Python, developers can utilize various libraries such as pandas, sktime, pdmarima, and tsfresh. These tools provide efficient storage, retrieval, and manipulation of time-series data, enabling the analysis of complex patterns and trends. By leveraging Timescale, a powerful open-source relational database for time-series and analytics, developers can execute Python code directly in the database, accelerating their development process and improving the efficiency of time-series data management.