Collections/The Data Stack, Start to Finish
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The Data Stack, Start to Finish

Five open-source workhorses that carry data from raw files through heavy processing all the way to the dashboard and the chart.

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Created by Sanjeet Pal Singh
updated 5 hours ago
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Projects in this collection

Ranked by the curator, with their reason for including each one.

1
pandasPython
★ New49.7K GitHub starsBSD-3-Clause
Why Sanjeet Pal Singh included it: “It is the default toolkit for wrangling tabular data in Python, and almost every analysis workflow starts or ends with a DataFrame.”
2
duckdbC++
★ New41K GitHub starsMIT
Why Sanjeet Pal Singh included it: “An analytical SQL engine that runs inside your process with no server to manage, making it the fastest way to query local files and DataFrames at scale.”
3
sparkScala
★ New44K GitHub starsApache-2.0
Why Sanjeet Pal Singh included it: “When a single machine is no longer enough, Spark remains the reference engine for distributed batch and streaming analytics across the cluster.”
4
supersetPython
★ New74.7K GitHub starsApache-2.0
Why Sanjeet Pal Singh included it: “A full business-intelligence layer that lets analysts explore any SQL database and publish interactive dashboards without leaving the browser.”
5
d3Shell
★ New113.7K GitHub starsISC
Why Sanjeet Pal Singh included it: “The foundational library for bespoke, data-driven graphics on the web, and the engine underneath countless higher-level charting tools.”