Hex is a cloud-based collaborative data platform where SQL, Python, and no-code analyses live side-by-side in notebook projects. Cells chain together through a reactive DAG engine, so upstream changes automatically re-execute dependent cells; finished notebooks publish as polished interactive apps with a single click. This cheat sheet covers everything from cell types and SQL chaining through scheduling, secrets, semantic models, embedded analytics, and the Magic AI suite.
What This Cheat Sheet Covers
This topic spans 15 focused tables and 213 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Cell Types
Every Hex project is built from typed cells, each with its own execution engine and output. Choosing the right cell type for the job — rather than forcing everything into Python — keeps notebooks readable and lets Hex optimize execution.
| Type | Example | Description |
|---|---|---|
SELECT user_id, SUM(revenue) AS total FROM orders GROUP BY 1 | • Queries a connected data warehouse or an in-memory DataFrame • result exposed as a named Pandas DataFrame automatically | |
import pandas as pddf = df_orders[df_orders.revenue > 0] | • Runs arbitrary Python 3 • all cell outputs (DataFrames, variables, figures) are available to downstream cells | |
# Analysis SummaryRevenue grew **12 %** QoQ. | • Renders GitHub-flavored Markdown • used for narrative, headings, and callout text between analytical cells | |
(drag columns onto X/Y axes in the no-code chart builder) | • No-code drag-and-drop visualization powered by Vega-Lite • auto-suggests chart types based on data shape |