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How to use DLT: what you can do with it

What is DLT? dlt (data load tool) is an open source Python library that loads data from APIs, SQL databases, files, DataFrames and Arrow tables into typed datasets in more than twenty destinations. It handles credentials, DDL in the target dialect, type mapping, staging and schema drift, and adds decorators for incremental loading, merge write dispositions and schema contracts. Loaded data reads back through a Dataset API or as Ibis expressions.

How do I use it? Install the library and the destination extra with pip install dlt and pip install "dlt[duckdb]"; then build a source with rest_api_source, giving the client base_url and a resource with its endpoint path; create the pipeline with DLT.pipeline, naming pipeline_name, destination and dataset_name, then call pipeline.run(source). Full steps below.

2 people shared what they built with DLT. See what people are building

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What do you want to do?

3 use cases

What people are building with DLT

  • Built withthe web

    Replaced hand written GitHub API scripts with a dlt pipeline into DuckDB

    He had been looping over the GitHub API with requests, handling pagination and nested JSON by hand, and swapped the extraction layer for dlt, which infers the schema, unnests responses into relational tables and keeps incremental state so later runs only fetch new issues and pull requests. The data lands in a local DuckDB file and dbt transforms it in bronze, silver and gold layers.

    DLT on GitHubTry it: Pull a REST API into a local database

    By Jitse-Jan (jitsejan.com) on the web

    View original post on the web

  • Tutorialthe web

    Ingested near real time and historical IoT feeds into DuckDB with dlt

    She walks through building two pipelines for a fleet IoT API with rest_api_source, one for the near real time GPS feed and one for a historical window, with a bearer token read from secrets.toml and pagination handled by dlt. Both load into DuckDB, where she queries the generated tables and exports the history to CSV.

    DLT on GitHubTry it: Pull a REST API into a local database

    By Sophia Parafina (spara_50) on the web

    View original post on the web

Real builds shared on X, Hacker News, Reddit, DEV and GitHub, summed up by Deduped from each post and its links; each card links to the original post. See what people are building with other repos

Quick start with DLT

  1. Check you have what it needs: Python 3.10 through 3.14, with 3.14 support described as experimental; `pip install dlt` plus the extras for your sources and destinations; Credentials for your destination in secrets.toml or environment variables.
  2. Open dlt-hub/dlt on GitHub and read the README and license.
  3. Install the library and the destination extra with pip install dlt and pip install "dlt[duckdb]"
  4. Build a source with rest_api_source, giving the client base_url and a resource with its endpoint path
  5. Create the pipeline with DLT.pipeline, naming pipeline_name, destination and dataset_name, then call pipeline.run(source)
  6. Read the data straight back with pipeline.dataset() and .df()

These steps come from our top use case, Pull a REST API into a local database, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forData and analytics engineers who want pipelines as plain Python instead of a platform.
  • Why it is hot6k stars, picked up by one AI source we track
  • Sourcegithub.com/dlt-hub/dlt ยท Back to Hot repos

Requirements

  • Python 3.10 through 3.14, with 3.14 support described as experimental
  • `pip install dlt` plus the extras for your sources and destinations
  • Credentials for your destination in secrets.toml or environment variables

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Questions about DLT

What is DLT?

dlt (data load tool) is an open source Python library that loads data from APIs, SQL databases, files, DataFrames and Arrow tables into typed datasets in more than twenty destinations. It handles credentials, DDL in the target dialect, type mapping, staging and schema drift, and adds decorators for incremental loading, merge write dispositions and schema contracts. Loaded data reads back through a Dataset API or as Ibis expressions. It is an open-source project on GitHub: https://github.com/dlt-hub/dlt.

How do I use DLT?

Install the library and the destination extra with pip install dlt and pip install "dlt[duckdb]"; then build a source with rest_api_source, giving the client base_url and a resource with its endpoint path; create the pipeline with DLT.pipeline, naming pipeline_name, destination and dataset_name, then call pipeline.run(source). The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is DLT for?

Data and analytics engineers who want pipelines as plain Python instead of a platform.

What can I do with DLT?

3 use cases so far, for example: Pull a REST API into a local database; Load only new rows and upsert on a key; Query what you loaded without writing SQL. Each has steps and a copy-paste agent prompt.

What do I need to run DLT?

Python 3.10 through 3.14, with 3.14 support described as experimental; `pip install dlt` plus the extras for your sources and destinations; Credentials for your destination in secrets.toml or environment variables.

What are alternatives to DLT?

Other hot AI repos in Data and analytics: Chronos Forecasting, Apify MCP Server, Crawlee Python, Timesfm, Torchgeo.

Is DLT safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.