data-formats

Ingest RDF data into sparq Graphs from Turtle, N-Triples, N-Quads, and TriG formats.

8|1|Updated Jun 7, 2026
One-click install
npx skills add https://github.com/sparq-org/sparq --skill data-formats-sparq-org
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-formats
Source: https://github.com/sparq-org/sparq/tree/main/skills/data-formats
Command: npx skills add https://github.com/sparq-org/sparq --skill data-formats-sparq-org

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sparq-core, sparq-hdt, oxrdf, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of ingesting RDF data into a sparq Graph, handling various formats efficiently, and allows for inexpensive, immutable snapshotting of the Graph for serving.

Core Features & Use Cases

  • Efficient RDF Ingestion: Supports Turtle, N-Triples, N-Quads, and TriG formats for RDF data loading.
  • Parallel and External-Memory Loading: Handles compressed dumps and parallel streaming for large datasets.
  • Graph Snapshots: Takes immutable, copy-on-write snapshots of the Graph for serving without reloading.
  • Use Case: When you need to efficiently load RDF data from multiple sources, this Skill provides a streamlined process, allowing you to create a consistent, immutable view of the data for analysis or serving.

Quick Start

Load RDF data from 'data.nt' using the 'sparq-cli' and take a snapshot of the resulting Graph.

Frequently Asked Questions about data-formats

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

FAQPage Schema
How do I load large RDF datasets into sparq Graphs efficiently?▼

To load large RDF datasets into sparq Graphs efficiently, use parallel and external-memory loading mechanisms that handle compressed dumps and parallel streaming for large-scale data ingestion.

What RDF formats are supported for ingesting data into a sparq Graph?▼

RDF data ingestion into a sparq Graph supports Turtle, N-Triples, N-Quads, and TriG formats, allowing you to load data from multiple sources using these standard serializations.

How does graph snapshotting work for serving sparq Graphs?▼

Graph snapshotting creates immutable, copy-on-write snapshots of the sparq Graph, allowing you to serve a consistent view of the data for analysis without needing to reload the dataset.

Do I need sparq-core and sparq-hdt to ingest RDF data?▼

Yes, RDF data ingestion and graph snapshotting require the sparq-core and sparq-hdt crates, along with oxrdf, to handle formats and provide efficient external-memory loading.

What is the best way to create an immutable view of an RDF graph for analysis?▼

The best way to create an immutable view of an RDF graph for analysis is to take a copy-on-write snapshot after ingesting the data, which provides a consistent serving view without reloading.

Can I load compressed RDF dumps using parallel streaming in sparq?▼

Yes, you can load compressed RDF dumps using parallel streaming mechanisms designed for external-memory loading, enabling efficient ingestion of large datasets into sparq Graphs.