dlt — Data Load Tool for Python ELT Pipelines

Introduction

dlt makes data ingestion as simple as writing a Python function. Instead of configuring heavyweight ELT platforms or writing custom loaders, you create Python generators that yield data and dlt takes care of the rest: schema inference, nested data normalization, incremental loading, and reliable state management. It was designed for data engineers who want code-first pipelines without the infrastructure overhead.

What dlt Does

• Loads data from any Python source (APIs, files, databases) into warehouses and lakes
• Automatically infers and evolves schemas as source data changes
• Normalizes nested JSON into flat relational tables with proper foreign keys
• Supports incremental loading with automatic state tracking and deduplication
• Writes to DuckDB, BigQuery, Snowflake, Redshift, Postgres, Databricks, and more

Architecture Overview

A dlt pipeline consists of a source (Python generator decorated with @dlt.source), a destination (warehouse or lake adapter), and a pipeline object that coordinates extraction, normalization, and loading. During extraction, dlt streams data into local files. The normalizer flattens nested structures into relational tables and infers column types. The loader bulk-inserts into the destination using optimized methods (COPY, staging files). Pipeline state is stored alongside the data for incremental tracking.

Self-Hosting & Configuration

• Install with pip install dlt[destination] where destination is duckdb, bigquery, snowflake, etc.
• Create a pipeline with dlt.pipeline() specifying name, destination, and dataset
• Configure credentials in .dlt/secrets.toml or environment variables
• Use @dlt.source and @dlt.resource decorators to define reusable data sources
• Deploy to Airflow, Dagster, Modal, or GitHub Actions with dlt deploy command

Key Features

• Schema inference and evolution with automatic type detection
• Nested JSON normalization into relational tables with generated keys
• Incremental loading with built-in cursor and merge strategies
• 30+ verified sources (Stripe, Slack, SQL databases, REST APIs, etc.)
• REST API source that builds pipelines from OpenAPI specs or simple config

Comparison with Similar Tools

• Airbyte — UI-driven ELT platform with managed connectors; dlt is code-first Python with no infrastructure required
• Singer/Meltano — tap/target specification with separate processes; dlt runs everything in a single Python process
• Fivetran — managed SaaS ELT; dlt is open source and runs anywhere Python runs
• Pandas — data manipulation library; dlt handles full ELT lifecycle including schema management and incremental loading
• SQLAlchemy — database toolkit; dlt abstracts away the ORM layer and handles the full ingestion pipeline

FAQ

Q: Do I need a running service to use dlt? A: No. dlt is a Python library you call from scripts, notebooks, or orchestrators. There is no daemon or UI required.

Q: How does dlt handle schema changes? A: dlt tracks schemas and auto-evolves them. New columns are added to the destination table. Type changes are handled by configured policies (coerce, discard, or freeze).

Q: Can dlt handle large datasets? A: Yes. dlt streams data to local files during extraction and uses bulk loading methods (staged files, COPY commands) for efficient writes to warehouses.

Q: What if my source is not in the verified sources list? A: Write a custom source as a Python generator. The REST API source covers most HTTP APIs with minimal configuration.

Sources

• https://github.com/dlt-hub/dlt
• https://dlthub.com/docs