Data processing can be a critical task in business decision-making, especially as datasets grow in size and complexity. To optimize data processing, improving data locality by performing calculations close to the data is crucial. This approach reduces overall latency, positively impacting the user experience and system performance metrics. Downsampling data aims to make it more manageable by reducing its granularity. Data locality refers to processing data as close as possible to where it's stored. By analyzing how data travels from querying in the database to visualizing data in the system, it becomes clear that performing calculations close to the data can significantly reduce latency and improve performance. The use of hyperfunctions, such as those provided by TimescaleDB, can bring data analysis superpowers to SQL, increasing data processing power and speed while reducing network traffic between the application and database. This approach allows for more flexibility in data analysis and relieves the application workload, ultimately releasing I/O waiting time with fewer context-switching threads. By leveraging data locality and using hyperfunctions like those provided by TimescaleDB, businesses can accelerate data-driven decisions, save money, and improve user experience.