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.