PrismNote

SQL analytics notebooks that run offline. On your computer. No cloud required.

Write SQL queries in a notebook that saves locally, switch between 8 warehouses (Snowflake, BigQuery, Redshift, Postgres, MySQL, DuckDB, Spark, ClickHouse) without changing code, and execute everything offline. Built for data scientists and engineers who want control.

30-Second Start

from prismnote import Notebook

# Create notebook (saved locally)
nb = Notebook("my_analysis.pnb")

# Connect to any warehouse
nb.connect("snowflake", account="xy12345", warehouse="compute_wh")

# Write SQL directly
result = nb.query("""
    SELECT customer_id, COUNT(*) as purchases
    FROM orders
    GROUP BY customer_id
    ORDER BY purchases DESC
""")

# Results in Python
print(result)

Why PrismNote?

The Problem:

• Jupyter notebooks for SQL are clunky (no syntax highlighting, no auto-complete)
• Cloud notebooks (Colab, Databricks) lock your data in the cloud
• Switching between databases requires rewriting code
• No offline support (can't work on the plane)

The Solution:

• Native SQL notebooks with full IDE features
• Runs completely offline (no data leaves your machine)
• Switch warehouses without changing a line of code
• Version control your analysis (plain text format)

Key Features

• 8 Warehouse Support: Snowflake, BigQuery, Redshift, Postgres, MySQL, DuckDB, Spark, ClickHouse
• Local-First: Notebooks saved on your computer, not the cloud
• Offline Execution: Run queries without internet connection (for local databases)
• SQL + Python: Mix SQL queries with Python for analysis
• Auto-Complete: Full SQL syntax highlighting and warehouse-aware suggestions
• Result Visualization: Built-in charts, tables, and statistical summaries
• Export Ready: Save results as CSV, Parquet, JSON

Real-World Use Cases

Data Exploration:

# Quickly explore a new dataset
nb = Notebook("exploration.pnb")
nb.connect("bigquery", project="my-project")

# Instant feedback, no context switching
nb.query("SELECT * FROM dataset LIMIT 100")

Cross-Warehouse Analysis:

# Pull from two different databases
results_snowflake = nb.query_snowflake("SELECT * FROM sales")
results_postgres = nb.query_postgres("SELECT * FROM products")

# Combine in Python
merged = pd.merge(results_snowflake, results_postgres, on='id')

Offline Analysis:

# DuckDB for local, offline data science
nb = Notebook("offline_analysis.pnb")
nb.connect("duckdb", path="local.db")
nb.query("SELECT * FROM my_local_data")  # Works on airplane

Warehouse Support Matrix

Installation

pip install prismnote
# or with uv
uv pip install prismnote

Documentation

• Quick Start Guide — Get running in 5 minutes
• Warehouse Guide — Connect to your database
• SQL Reference — Warehouse-specific SQL features
• Examples — Real-world notebooks

License

Proprietary License - Free to use with explicit attribution. See LICENSE.

PrismNote v2.0.0 | Local-first SQL notebooks | Python 3.10+