Get to know your data's data - EDA in Tinybird

Exploratory Data Analysis (EDA) is crucial for understanding and ensuring the integrity of data before diving into complex analyses, similar to checking a deck of cards before a game. Traditionally, EDA involved labor-intensive SQL processes for examining data quality and structure, such as counting null values and checking distributions. Databricks streamlines this with its Data Profiles feature, providing an accessible dashboard that displays essential column-level statistics. Tinybird, in response to user feedback, introduced Tinybird Forward and Explorations, which replace the previous SQL-heavy Playgrounds with a chat-style prompt interface that automatically generates queries to profile data. This new feature enhances speed, visibility, and dynamic maintainability, allowing users to easily verify data quality, detect outliers, and modify queries across projects. As Tinybird continues to develop Explorations, it has the potential to include features like histograms, automatic outlier detection, and LLM-powered summaries, offering a more comprehensive data profiling tool.