dlt (data load tool)
dlt is a lightweight, open-source Python library for loading data from any source into well-structured datasets with automatic schema inference.
dlt (data load tool) is a lightweight, production-ready Python library that simplifies extracting and loading data from any source into structured datasets.
Key Features:
- Auto Schema Inference - Automatically detect and evolve schemas as your data sources change.
- Any Source to Any Destination - Load from REST APIs, databases, files, and Python data structures.
- No Infrastructure - Import as a Python library with no backends, containers, or complex setup.
- LLM-Ready - Build pipelines with AI code editors using dltHub Context assets and skills.
- Pipeline Composition - Chain extract and load steps into reproducible, version-controlled pipelines.
Whether you're ingesting API data, loading files into a lakehouse, or building AI-powered ETL, dlt provides a Pythonic way to move data reliably.
Web scraping workflow
How to use dlt (data load tool) in a web scraping pipeline
The Python library dlt loads data from Python sources into configured destinations and normalizes its structure. It can connect an existing extraction routine to a warehouse without replacing the routine that acquires the pages.
A practical workflow
- A separate HTTP or browser scraper yields dictionaries with extracted fields, stable identifiers and source metadata.
- Pass those records to a dlt pipeline, choosing the destination and write disposition appropriate to the dataset.
- Inspect the load result and expose the resulting tables to transformation or analytical jobs.
When it fits
Use it when collection code already produces Python records and the remaining problem is reliable loading and schema handling. Define record identity and whether each run appends observations or updates current state before choosing the loading behavior.
Boundaries
dlt is not a universal website parser or JavaScript renderer. Normalization is structural, not proof of semantic accuracy. Destination credentials, extraction validation and treatment of schema drift remain your responsibility, and a successful load should retain enough provenance to trace a row to its capture.
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Our productRepository
dlt-hub/dlt
Stars
6KForks
624Last commit
2026-10-15License
Apache-2.0Language
Python
Metrics recorded 2026-10-16
Repository details
License source: Repository license file
Frequently asked questions
What is the Python dlt library used for?
The Python dlt library extracts data from sources, normalizes it and loads it into a configured destination. It supports schema inference and evolution for data-loading pipelines.
Sources (checked ): Python dlt introduction
How do I set up a Python dlt pipeline?
A dlt pipeline specifies a destination and dataset for loading data. For the tutorial’s local DuckDB example: install dlt with its DuckDB extra, import dlt, create dlt.pipeline with pipeline_name, destination="duckdb" and dataset_name, then call pipeline.run with your data and table_name. Inspect the returned load_info for completed or failed loads.
Sources (checked ): dlt API tutorial; Python dlt introduction
Sources
Verified against the dlt-hub/dlt repository and the PyPI package.