dlt

Automate data ingestion pipelines from APIs, databases, and files into destinations.

24|11|Updated Feb 19, 2026
One-click install
npx skills add https://github.com/clawdata/clawdata --skill dlt-clawdata
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dlt
Source: https://github.com/clawdata/clawdata/tree/main/skills/dlt
Command: npx skills add https://github.com/clawdata/clawdata --skill dlt-clawdata

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build data ingestion pipelines with dlt to extract data from APIs, databases, and files and load it into a destination data store for analytics.

Core Features & Use Cases

  • Initialize and run end-to-end pipelines with simple commands
  • Support incremental loads and multiple data sources (APIs, SQL databases, files)
  • Deploy to common destinations (DuckDB, PostgreSQL, BigQuery, Snowflake, Redshift, Databricks)

Quick Start

Install the dlt package, create a simple pipeline, and run it to ingest data.

Frequently Asked Questions about dlt

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

FAQPage Schema
How do I build a data ingestion pipeline from an API to a SQL database?

You can build a data ingestion pipeline by initializing it with simple commands to extract data from APIs and load it directly into SQL databases. This automates end-to-end ETL processes for analytics teams using various sources.

What is the best way to automate incremental loading from APIs and files?

Automating incremental loading is handled natively by the pipeline execution flow. It ingests data from APIs, databases, and files, applying incremental loads to efficiently update destinations like DuckDB, PostgreSQL, or Snowflake.

Does this data ingestion approach support BigQuery, Snowflake, and Redshift destinations?

Yes, this data ingestion approach supports deploying pipelines to BigQuery, Snowflake, and Redshift. It also loads data into DuckDB, PostgreSQL, and Databricks for analytics workflows.

How do I set up an ETL pipeline for analytics teams using multiple data sources?

To set up an ETL pipeline, install the required Python library, initialize the pipeline, and run it to ingest data from APIs, SQL databases, and files. This automates extraction and loading into your specified data destinations.

Can I extract data from SQL databases and load it into DuckDB or Databricks?

Yes, you can extract data from SQL databases and load it into DuckDB or Databricks. The pipeline supports multiple data sources and destinations, enabling automated ELT workflows across different environments.