# dlt-hub/dlt

data load tool (dlt) is an open source Python library that makes data loading easy 🛠️

Repository: https://github.com/dlt-hub/dlt
Canonical: https://ross.abutalabs.com/products/dlt
Homepage: https://dlthub.com/docs
Language: Python
License: Apache-2.0
License Family: permissive
Topics: data, python, data-engineering, data-lake, data-loading, data-warehouse, elt, extract, load, transform
Last push: 2026-08-26T21:46:43+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 97, longevity 100
- inputs: {"age_days": 1680, "days_push": 7, "days_rel": 22, "gap_med": 13.5, "n_releases_24m": 45}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5778, forks 590 (observed 2026-08-28T04:09:29.554332+00:00)

## What it is
dlt (data load tool) is an open-source Python library for building ELT data pipelines that extract data from REST APIs, SQL databases, cloud storage, or Python data structures and load it into destinations like DuckDB, BigQuery, Snowflake, or S3. It automatically infers schemas, normalizes nested data, and handles incremental loading, schema evolution, and schema contracts.

## Use cases
- load data from a REST API into a database
- build an ELT pipeline in Python
- ingest JSON from APIs into BigQuery or Snowflake
- load SQL database tables into a data warehouse
- move files from S3 into a data lake
- incrementally sync new and updated records
- unnest nested JSON into relational tables
- run data pipelines in Airflow or serverless functions

## When to choose
- you need a Python-first, code-based alternative to GUI ETL tools like Fivetran or Airbyte
- you want automatic schema inference, typing, and normalization of messy source data
- you need incremental loading and schema evolution without maintaining pipeline code by hand
- you want pipelines that run anywhere Python runs, from notebooks to Airflow to Lambda

## When to avoid
- you need heavy in-pipeline transformations rather than extract-and-load (dlt focuses on EL, with transformations via dltHub or external tools)
- your team prefers no-code/low-code managed connectors over writing Python
- you need streaming (sub-second) ingestion rather than batch or micro-batch loads

## Facets
- artifact type: library
- maturity: stable
- function: etl, data-science, streaming, database, sdk
- domain: big-data, databases, analytics
- platform: python, cross-platform, serverless, cloud
- tags: elt, data-loading, data-pipelines, schema-inference, incremental-loading, rest-api, reverse-etl, data-warehouse, data-lake, data-engineering, automation

## Member repositories
- dlt-hub/dlt (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:29.554332+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:52:51.307543+00:00, confidence not recorded.
  - readme: https://github.com/dlt-hub/dlt (fetched 2026-08-28T04:09:29.554332+00:00, sha 599a62a9916f)
  - homepage: https://dlthub.com/docs (fetched 2026-08-29T08:48:18.944295+00:00, sha d1a69711504f)
  - site_page: https://dlthub.com/docs/reference/explainers/how-dlt-works (fetched 2026-08-29T08:48:18.964389+00:00, sha ba4df49b4e95)
  - site_page: https://dlthub.com/docs/devel/intro (fetched 2026-08-29T08:48:18.953253+00:00, sha 90224f5bfd14)
  - site_page: https://dlthub.com/docs/hub/getting-started/introduction (fetched 2026-08-29T08:48:18.955018+00:00, sha 7f6a9b9da3bd)
  - site_page: https://dlthub.com/docs/examples (fetched 2026-08-29T08:48:18.956902+00:00, sha b49962b6fdb1)
  - site_page: https://dlthub.com/docs/tutorial/education (fetched 2026-08-29T08:48:18.958394+00:00, sha e0a2daf67b93)
  - site_page: https://dlthub.com/docs/release-highlights (fetched 2026-08-29T08:48:18.959865+00:00, sha ba76121f7efa)
  - site_page: https://dlthub.com/docs/reference/installation (fetched 2026-08-29T08:48:18.961335+00:00, sha db5278131b0c)
  - site_page: https://dlthub.com/docs/tutorial/rest-api (fetched 2026-08-29T08:48:18.962795+00:00, sha 3c18ea698a66)
- Data as of 2026-08-30T08:39:29.467469+00:00.