hdf5-on-s3-cached

Cache HDF5 files from S3 locally and convert subsets to Parquet.

4|1|Updated Apr 11, 2026

Search Tags:#caching #s3 #parquet #data-engineering #hdf5 #dask

Ready to run

npx skills add https://github.com/arm2arm/AstroAgentAssistant --skill hdf5-on-s3-cached

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hdf5-on-s3-cached
Source: https://github.com/arm2arm/AstroAgentAssistant/tree/main/python/hdf5-on-s3-cached
Command: npx skills add https://github.com/arm2arm/AstroAgentAssistant --skill hdf5-on-s3-cached

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Access HDF5 files stored on S3 by creating a reliable local cache first, extracting reusable subsets, and converting repeated tabular work products to local Parquet.

Core Features & Use Cases

Cache-first access to remote HDF5 datasets to avoid repeated S3 latency.
Local subset extraction and conversion to Parquet for efficient downstream analysis.
Scales from small experiments to large workloads using optional Dask when needed.

Quick Start

Create a local cache of the HDF5 dataset on S3, read it locally, and convert the results to Parquet for fast downstream analysis.

Frequently Asked Questions about hdf5-on-s3-cached

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I cache HDF5 files on S3 for faster local data access?▼

To cache HDF5 files on S3, you can establish a cache-first read mechanism using h5py and pandas. This approach stores remote HDF5 datasets locally, avoiding repeated S3 latency during repeated scientific analyses.

Why does accessing HDF5 data on S3 take so long and how can I speed it up?▼

Accessing HDF5 data on S3 is slow due to remote object store latency. You can speed it up by creating a reliable local cache first, then reading subsets directly from local storage for repeated analyses.

Can I convert HDF5 subsets from S3 into Parquet for downstream analytics?▼

Yes, you can convert HDF5 subsets from S3 into Parquet. After caching the remote HDF5 file locally, you can extract reusable tabular subsets and convert them to local Parquet for efficient downstream analysis.

Do I need Dask to process large HDF5 workloads stored on S3?▼

You do not always need Dask, but it is optional for large workloads. The caching process scales from small experiments to large data engineering tasks using Dask when additional distributed computing power is needed.

What is the best way to extract a subset from a remote HDF5 dataset without downloading the entire file?▼

The best way to extract a subset from a remote HDF5 dataset is to use a local cache. This enables cache-first reads and targeted subset extraction using Python tools like h5py, preventing full file downloads.

What Python tools are required to read and convert HDF5 data from S3?▼

The required Python tools to read and convert HDF5 data from S3 are h5py and pandas. You also need a local cache directory, and optionally Dask for handling large data engineering workloads.