Adapt Postgres queries to Tinybird

Postgres is a robust OLTP database, but its real-time query performance on large datasets is limited, which is where Tinybird excels. This guide explores how to adapt Postgres queries to run efficiently on Tinybird, drawing inspiration from Haki Benita's work on SQL for data analysis. It covers the adaptation of common table expressions, demonstrating that while Postgres CTEs are versatile, they can be substituted with Tinybird's subqueries and Pipes for similar functionality. Despite Tinybird's limitations with certain SQL functions like generate_series, equivalent operations can be performed using its unique functions such as arrayJoin and range. The guide also explains how to perform tasks like joining data, random sampling, descriptive statistics, and linear regression on Tinybird, emphasizing its specialized capabilities for handling real-time analytics on large datasets. Additionally, it highlights Tinybird's tools for managing time-series data, handling missing values, and performing binning and histograms, showcasing its optimization for scalable, real-time data analysis.