Notebook Analytics Workbench

What this collection sets up

This collection is a practical notebook analytics workbench: write analysis in reviewable notebooks, query local files without standing up a warehouse, transform tabular data efficiently, load approved datasets, validate important columns, and share reviewed results as lightweight apps.

It is not a privacy-first research workflow and it is not a full data engineering platform. The focus is the analyst loop from raw files or approved datasets to reproducible notebook, validated result, and shareable report.

Layers

1. Notebook and local query layer

• marimo keeps Python notebooks in a git-friendly source format and supports notebook, script, app, SQL, and reactive execution workflows.
• duckdb gives notebooks and scripts an embedded analytical SQL engine for local files, Parquet, CSV, joins, and single-file analysis.
• polars provides a fast DataFrame engine for filtering, joins, transformations, lazy execution, streaming, and export preparation.

2. Dataset intake and quality checks

• hugging-face-datasets loads, streams, inspects, and preprocesses Hub or local datasets with cache and split/configuration controls.
• great-expectations records data-quality expectations, validation definitions, checkpoints, and Data Docs before outputs are trusted.

3. Sharing reviewed analysis

• streamlit turns reviewed scripts into interactive data apps, dashboards, reports, and analytical interfaces when a notebook needs a human-facing view.

Suggested order

Start with Marimo so analysis lives in reviewable source. Add DuckDB for local SQL against files and Polars for DataFrame transformations. Load external or large datasets only after their source, license, schema, and cache behavior are approved. Add Great Expectations checks around key columns and metrics before sharing results. Use Streamlit last, after the analysis is stable and the data exposure rules are clear.

Analysis checklist

• {"task": "Sources are inventoried", "description": "Files, datasets, licenses, revisions, credentials, and cache paths are known"}
• {"task": "Notebook is reproducible", "description": "Dependencies, paths, parameters, and execution order are clear enough for review"}
• {"task": "Queries are bounded", "description": "Large scans, joins, exports, and remote downloads have limits and expected sizes"}
• {"task": "Data quality is checked", "description": "Important columns, row counts, nulls, ranges, and joins have validation expectations"}
• {"task": "Outputs are reviewed", "description": "Charts, CSVs, notebooks, Data Docs, and apps are checked before sharing"}
• {"task": "Retention is explicit", "description": "Caches, DuckDB files, Streamlit state, screenshots, and exports have cleanup rules"}

Source and references

• Marimo getting started: https://docs.marimo.io/getting_started/
• DuckDB clients overview: https://duckdb.org/docs/stable/clients/overview
• Polars getting started: https://docs.pola.rs/user-guide/getting-started/
• Hugging Face Datasets documentation: https://huggingface.co/docs/datasets/index
• Great Expectations introduction: https://docs.greatexpectations.io/docs/core/introduction/
• Streamlit get started: https://docs.streamlit.io/get-started

Duplicate check

Checked existing collections, tools, skills, commands, hooks, guides, open PRs, closed PRs, and issue history for notebook-analytics-workbench, data analysis notebook workflow, Marimo, DuckDB, Polars, Hugging Face Datasets, Great Expectations, Streamlit, analytics workbench, and notebook dashboards. privacy-first-research-workflow uses Marimo, DuckDB, and Polars inside a privacy/redaction/research workflow. data-engineering-suite focuses on ETL, databases, cloud services, and pipeline infrastructure. This entry is narrower: it covers the analyst notebook loop from data intake to local query, transformation, quality checks, and reviewed sharing.

Disclosure

Editorial collection. No paid placement or affiliate link is used.