optimizing-parquet-storage

optimizing-parquet-storage is a skill for Claude Code from Unknown-333/awesome-data-engineering-skills. It costs 84 tokens per session (722 once invoked), scanned A, original, no licence file.

A guide for storing analytics data efficiently in Parquet, a file format that stores table columns separately. It covers file layout, compression, partitions, row groups, and how readers skip unnecessary data.

In plain words
What is it for?
Use it to choose Snappy or ZSTD compression, size files and row groups, organize partitions, and improve column pruning and predicate pushdown. It also addresses dictionary encoding and the small-files problem.
Why use it?
It helps reduce slow or expensive reads caused by poor file sizes, unsuitable compression, bad layouts, or too many small files. Better organization lets analytics tools read less data.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the data-engineering-skills plugin — 37 skills shipped together

Good fit Use it to choose Snappy or ZSTD compression, size files and row groups, organize partitions, and improve column pruning and predicate pushdown. It also addresses dictionary encoding and the small-files problem.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/unknown-333/awesome-data-engineering-skills/optimizing-parquet-storage
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Unknown-333/awesome-data-engineering-skills --skill optimizing-parquet-storage
Clone the repo
git clone --depth 1 https://github.com/Unknown-333/awesome-data-engineering-skills

Made for: Claude Code.

Or install data-engineering-skills, the plugin that ships this one along with the rest of its 37 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for optimizing-parquet-storage

README.md
[![agentmods](https://agentmods.dev/badge/skills/unknown-333/awesome-data-engineering-skills/optimizing-parquet-storage/github.svg)](https://agentmods.dev/skills/unknown-333/awesome-data-engineering-skills/optimizing-parquet-storage)
Your own site
<a href="https://agentmods.dev/skills/unknown-333/awesome-data-engineering-skills/optimizing-parquet-storage"><img src="https://agentmods.dev/badge/skills/unknown-333/awesome-data-engineering-skills/optimizing-parquet-storage/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for optimizing-parquet-storage

Your own site · 80×15
<a href="https://agentmods.dev/skills/unknown-333/awesome-data-engineering-skills/optimizing-parquet-storage"><img src="https://agentmods.dev/badge/skills/unknown-333/awesome-data-engineering-skills/optimizing-parquet-storage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 722 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00084 $0.00722
Opus 5 $0.00042 $0.00361
Sonnet 5 $0.00017 $0.00144
Haiku 4.5 $0.00008 $0.00072

Measured 9d ago against content hash b3b2624dfb79, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

optimizing-parquet-storage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/optimizing-parquet-storage/SKILL.md · 68 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 68 lines · 84 tokens per session scan A b3b2624dfb79

Subscribe to this mod's changes

optimizing-parquet-storage is a skill published in the GitHub repository Unknown-333/awesome-data-engineering-skills (17 stars, last pushed 10d ago), with no licence file. It adds 84 tokens to every session and 722 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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