What is lakehouse-table-format-engineering?

A guide to designing tables for a data lakehouse using formats such as Iceberg, Delta, or Hudi. It covers schemas, partitions, updates, snapshots, compaction, and batch or streaming access.

Use it to design lakehouse datasets, choose a table format, change partitioning, manage snapshots, compact data, or build incremental pipelines.

It helps teams choose and change a table format without overlooking compatibility, storage behavior, or how different processing tools interact with the data.

lakehouse-table-format-engineering — Skill for Claude Code and Codex b

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 vaquarkhan/data-engineering-agent-skills --skill lakehouse-table-format-engineering

Clone the repo

git clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skills

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/lakehouse-table-format-engineering/github.svg)](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/lakehouse-table-format-engineering)

Your own site

<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/lakehouse-table-format-engineering"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/lakehouse-table-format-engineering/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/lakehouse-table-format-engineering"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/lakehouse-table-format-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 603 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 original 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.

Model	Per session	Once invoked
Fable 5.1	$0.00049	$0.00603
Opus 5	$0.00024	$0.00302
Sonnet 5	$0.00010	$0.00121
Haiku 4.5	$0.00005	$0.00060

Measured 6d ago against content hash 1b83eadd8371, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

lakehouse-table-format-engineering 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 6d 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/lakehouse-table-format-engineering/SKILL.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Lakehouse Table Format Engineering

Overview

Use this skill when the storage layer itself is an architecture decision. It helps agents reason about Iceberg, Delta, Hudi, table evolution, partitioning, compaction, snapshot behavior, and how batch and streaming jobs interact with shared lakehouse tables.

When to Use

choosing between Iceberg, Delta, and Hudi
designing lakehouse datasets with update or merge behavior
changing partition strategy, snapshot retention, or compaction behavior
integrating Spark, Flink, or warehouse readers with shared table formats
implementing CDC-style or incremental lakehouse pipelines

Do not use this for unmanaged flat-file datasets that are not intended to behave like governed tables.

Workflow

Define the table contract. Include:
- grain and keys
- mutation model
- read patterns
- retention and snapshot policy
- compatibility requirements across engines
Pick the table format intentionally.
- Iceberg: strong open-table interoperability and metadata-driven planning
- Delta: strong lakehouse ergonomics and transactional patterns in Databricks-centered stacks
- Hudi: strong incremental and record-level update use cases
Design physical layout for lifecycle, not only day-one queries. Consider:
- partition evolution
- small-file control
- compaction
- metadata growth
- merge and delete behavior
Align compute engines with the table behavior. Batch and streaming writers must not conflict silently on checkpoints, commits, or schema changes.
Define maintenance and recovery operations. Table formats require operational housekeeping, not just one-time creation.

Common Rationalizations

Rationalization	Reality
"A table format is just a storage detail."	Format choice affects interoperability, maintenance, mutation semantics, and long-term cost.
"We can decide compaction later."	Small files and metadata bloat become operational pain quickly.
"Any engine can read and write the table the same way."	Cross-engine support varies, especially for advanced write and mutation behavior.

Read the full file on GitHub · 73 lines

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.

6d ago First seen · 73 lines · 49 tokens per session scan A 1b83eadd8371

Subscribe to this mod's changes

lakehouse-table-format-engineering is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (43 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 603 once invoked, about $0.0002 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 2026-09-03.

Data and AI Databases agent-skills ai-agents apache-spark claude cursor data-engineering

mongodb-atlas-stream-processing

Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for…

fcakyon/claude-codex-settings · 82 tokens

mongodb-search-and-ai

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG…

fcakyon/claude-codex-settings · 132 tokens

lakehouse-table-format-engineering