Open Source Series: What is dlt?

Modern, Pythonic ELT Built for the Data Engineer in Code

If you're a Python developer who enjoys clean, flexible workflows directly in code, dlt Hub (short for Data Load Tool) is a minimalist but powerful open-source ELT framework. It makes data ingestion feel like writing native Python scripts—perfect for embedding pipelines in modern cloud-native apps, internal data platforms, or notebooks.

Unlike UI-heavy tools, dlt Hub is a code-first framework built for developers and data engineers who want to stay close to Python while abstracting the hard parts of ELT like schema evolution, incremental loading, and deployment.

What is dlt?

dlt is an open-source Python library for building and running ELT pipelines. It’s designed for teams that want flexibility, composability, and native integration with modern tools like dbt, DuckDB, and BigQuery.

Instead of YAML configs or UIs, you define your pipelines using Python functions enriched by dlt's decorators and helpers.

dlt handles:

• Incremental loading
• Schema evolution
• Credential management
• Destination writes

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If you want full programmatic control without writing orchestration and ingestion from scratch, dlt hits a unique sweet spot.

Core Concepts

To understand dlt, it helps to get familiar with its building blocks:

A Simple Example

Here’s a minimal dlt pipeline that pulls GitHub issues and loads them into Snowflake.

import dlt 
import requests 

@dlt.resource(write_disposition="append") 
def github_issues(): 
    response = requests.get("https://api.github.com/repos/org/repo/issues") 
    yield from response.json() 

pipeline = dlt.pipeline( 
    pipeline_name="github_pipeline", 
    destination="snowflake", 
    dataset_name="github_data" 
) 

pipeline.run(github_issues())

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No YAML files, no complex config—the schema is inferred, the state is managed, and the data is loaded.

Supported Integrations

dlt is source-agnostic and ships with adapters for common data sources:

Who is dlt For?

dlt is best suited for:

• Python-native teams who want to stay in code, not GUIs
• Developers building internal tools or platforms
• Data engineers needing clean abstractions for incremental data loads
• Fast-moving startups and prototyping teams

Pros and Considerations

Why choose dlt?

Community and Evolution

While it’s newer than tools like Airbyte or Meltano, it’s growing rapidly.

• Active GitHub repo with frequent updates
• Solid documentation with code-first examples
• Expanding integrations and open ecosystem

Conclusion

dlt hub is the “developer’s ELT”—lean, composable, and great for embedding or scripting data ingestion. While it doesn’t have the vast native connector ecosystem of tools like Airbyte or Meltano, it makes it easy to build repeatable, API-based connections using Python. dlt excels in use cases where full-code and flexibility are the priority. If you're already building pipelines in python taking a further look at dlt may save you time.

In future posts, we'll compare it directly to Airbyte and Meltano and later we'll show you how you can productionize dlt.

Until then, you can try it out at dltHub.com or browse the code on GitHub.